{ "schema_version": "2.0", "generated_at": "2026-10-20", "generated_by": "Human-edited record published with Sneh Sinha's portfolio (Agent Mode). Not produced by an automated extractor.", "schema": { "status": "exactly one string from evidence_labels.values", "qualifiers": "optional list of further evidence_labels.values", "checked": "ISO date of the last verification, YYYY-MM-DD", "verification_url": "URL that evidences the claim", "ids": "roles use role-* · capabilities capability-* · projects project-* · experience experience-*", "roles_match": "target_roles in agent-card.json must project exactly to candidate.json#roles" }, "name": "Sneh Sinha", "headline": "AI Engineer", "canonical_url": "https://sneh30.github.io/portfolio/", "agent_profile_url": "https://sneh30.github.io/portfolio/", "dedicated_agent_mode_url": "https://sneh30.github.io/portfolio/agent/", "source_of_truth": [ "https://sneh30.github.io/portfolio/", "https://sneh30.github.io/portfolio/agent/", "https://sneh30.github.io/portfolio/assets/Sneh_Raj_Sinha_Resume.pdf", "GitHub API (github.com/Sneh30)", "LeetCode API (leetcode.com/u/sneh_sinha3)" ], "candidate": { "name": "Sneh Sinha", "alternate_name": "Sneh Raj Sinha", "role": "AI Engineer", "canonical_url": "https://sneh30.github.io/portfolio/", "agent_profile_url": "https://sneh30.github.io/portfolio/", "dedicated_agent_mode_url": "https://sneh30.github.io/portfolio/agent/", "focus": [ "AI Agents", "LLM Systems", "AI Infrastructure", "Computer Use", "Embedded AI", "Computer Vision" ], "positioning": "An AI engineer who builds AI systems to solve real-world problems.", "positioning_secondary": "His work spans the path from intelligence to execution — AI agents, LLM systems, software, infrastructure, cloud operations, computer vision, embedded systems, and physical-world applications.", "core_differentiator": "AI systems + real-world execution + a documented cadence of shipping across stacks: repeatedly turning AI capabilities into systems that actually do something, across abstraction levels from cloud APIs down to embedded firmware.", "location": "Bangalore, India", "availability": "Open to work (as displayed on the portfolio)", "summary": "Builds agents, multi-agent systems, LLM tooling and autonomous infrastructure, and has built embedded and safety-critical software alongside two real-world engineering engagements." }, "contact": { "email": "snehsinha30@gmail.com", "phone": "+91 7903907151", "phone_source": "résumé PDF (not displayed in Human Mode)", "github": "https://github.com/Sneh30", "linkedin": "https://www.linkedin.com/in/sneh30", "x": "https://x.com/sneh_sinha3", "leetcode": "https://leetcode.com/u/sneh_sinha3/", "portfolio": "https://sneh30.github.io/portfolio/", "agent_mode": "https://sneh30.github.io/portfolio/agent/", "resume_pdf": "https://sneh30.github.io/portfolio/assets/Sneh_Raj_Sinha_Resume.pdf" }, "education": [ { "institution": "Bangalore Institute of Technology", "location": "Bangalore, Karnataka", "degree": "B.E. in Robotics and Artificial Intelligence", "start": "2023-08", "expected_completion": "2027-08", "gpa": "8.0/10", "source": "résumé PDF published with this portfolio", "note": "Education is displayed in Human Mode (About section) and in the résumé PDF." } ], "experience": [ { "id": "experience-infrastructure", "title": "Infrastructure Engineer", "organisation": "Independent Engagement (employer not disclosed)", "type": "Independent engagement, on-site", "start": "2026-08", "end": "2026-08", "duration": "1 month", "problem": "Recurring infrastructure incidents and slow recovery on a 400+ node exam-facility network.", "responsibility": "Strengthen infrastructure operations end-to-end during a one-month on-site engagement, working independently.", "engineering_challenge": "Reducing both incident frequency and mean time to recovery in a live, deadline-driven environment.", "outcome": "Infrastructure incidents and average recovery time cut by ~40% versus the prior comparable period (site notes a prior period of over 5 days).", "scale": { "nodes": "400+", "tenure": "1 month", "mode": "on-site" }, "skills_applied": [ "Observability", "Incident Response", "Prometheus", "Linux", "AWS", "Docker" ], "verification": { "status": "documented_experience", "qualifiers": [ "self_reported" ], "checked": "2026-09-30", "sources": [ "https://sneh30.github.io/portfolio/#experience", "https://sneh30.github.io/portfolio/assets/Sneh_Raj_Sinha_Resume.pdf" ], "limitation": "The portfolio does not currently provide sufficient evidence to verify this claim beyond the candidate's own statement: no employer name, raw before/after dataset, or third-party reference is published." }, "context": "Independent work in a live operational environment with a reported reliability outcome over a one-month engagement (400+ nodes, ~40% incident and recovery improvement, self-reported)." }, { "id": "experience-team-stratos", "title": "Software & Electronics Lead", "organisation": "Team Stratos", "type": "Team / competition engineering", "start": "2025-07", "end": "2026-05", "duration": "11 months", "problem": "A 50-member team's competition vehicle needed software and electronics integrated under real track conditions, with braking carrying the highest safety stakes.", "responsibility": "Own software and electronics integration for the vehicle.", "engineering_challenge": "Developing and deploying ABS (Anti-lock Braking System) firmware — the most safety-critical subsystem — where incorrect behaviour is a physical-safety risk.", "outcome": "ABS firmware developed, deployed and validated through track testing, SAE India scrutineering and competition.", "scale": { "team_size": "50", "tenure": "11 months" }, "skills_applied": [ "Embedded systems", "C/C++", "Vehicle electronics integration" ], "verification": { "status": "documented_experience", "checked": "2026-09-30", "sources": [ "https://sneh30.github.io/portfolio/#experience", "https://sneh30.github.io/portfolio/assets/Sneh_Raj_Sinha_Resume.pdf" ], "limitation": "Team project; the portfolio attributes the ABS firmware to Sneh but does not publish the code or a team reference." }, "context": "Embedded systems work with physical consequences: 11 months of full-vehicle integration and ABS firmware validated through track testing and SAE India scrutineering." } ], "projects": [ { "id": "project-opencode-cua", "name": "OpenCode CUA Platform", "one_line": "Computer-Use Agent platform that lets an unmodified OpenCode instance control a desktop GUI and browser through MCP servers.", "problem": "AI agents are normally confined to a terminal; driving real desktop and browser software requires perception, action, and recovery when actions fail.", "built": "Screen capture, macOS Accessibility APIs and browser automation behind MCP servers, plus verification and recovery engines.", "documented_result": "No numeric usage metric published; the documented result is an end-to-end computer-use loop with explicit verification and recovery stages.", "technologies_in_human_mode": [ "Python", "MCP", "macOS", "React" ], "categories": [ "Agentic AI", "Computer Use", "Computer Vision / Perception" ], "demonstrates": [ "capability-agents" ], "repository": "https://github.com/Sneh30/opencode-cua-platform", "verification": { "status": "public_repo", "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public" } }, { "id": "project-webcmd", "name": "webcmd (merged contribution)", "one_line": "Unified output-format parsing across all builtin commands of agentrhq/webcmd.", "problem": "Every builtin command handled -f/--format differently, an inconsistency that grows with each new command.", "built": "A single source of truth for output-format parsing applied across builtin commands, with 16 new tests and documentation updates.", "documented_result": "PR #190 merged 2026-08-13 with 16 commits; 15+ commands fixed; upstream project 2,620 stars (GitHub API, 2026-09-30).", "technologies_in_human_mode": [ "JavaScript", "Open Source", "CLI" ], "categories": [ "Developer / Open Source" ], "demonstrates": [ "capability-oss" ], "repository": "https://github.com/Sneh30/webcmd", "pull_request": "https://github.com/agentrhq/webcmd/pull/190", "verification": { "status": "external_validation", "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public" } }, { "id": "project-promptos", "name": "PromptOS", "one_line": "macOS desktop app that treats prompts as source code and compiles them through a multi-pass optimisation pipeline.", "problem": "Raw prompts are expensive and inconsistent; they should be rewritten and compressed before reaching the model.", "built": "Rust core with custom lexer, parser and AST, ten optimisation passes across four compilation modes, WASM plugin support, Llama.cpp local inference on Metal GPU, native SwiftUI shell.", "documented_result": "Résumé: up to 80–85% lower inference cost in Economy mode via multi-pass token compression, tested against frontier LLM pricing.", "technologies_in_human_mode": [ "Rust", "Swift", "Llama.cpp", "WASM", "Metal GPU" ], "categories": [ "LLM / AI Infrastructure" ], "demonstrates": [ "capability-llm" ], "repository": "https://github.com/Sneh30/promptos", "verification": { "status": "public_repo", "qualifiers": [ "self_reported" ], "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public", "metric_source": "résumé PDF" } }, { "id": "project-orchestra", "name": "Orchestra", "one_line": "Production-oriented AI research orchestration with 5 specialized LangGraph agents producing verified reports with confidence scoring.", "problem": "Automated research must be checkable, not confidently hallucinated.", "built": "Five-agent LangGraph pipeline (search, extract, verify, synthesize, critique), FastAPI services, PostgreSQL persistence, Prometheus instrumentation, test suite with deterministic CI mode, Docker Compose packaging.", "documented_result": "5 cooperating agents; verified reports with confidence scoring; one-command deployment. No traffic metric published.", "technologies_in_human_mode": [ "Python", "LangGraph", "FastAPI", "PostgreSQL", "Prometheus" ], "categories": [ "Agentic AI", "LLM / AI Infrastructure", "Autonomous Operations" ], "demonstrates": [ "capability-multagent", "capability-backend" ], "repository": "https://github.com/Sneh30/Orchestra", "verification": { "status": "public_repo", "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public", "source": "repository + résumé entry" } }, { "id": "project-deepops", "name": "DeepOps", "one_line": "Experimental autonomous SRE platform — active development.", "problem": "Infrastructure incident handling depends on people reading signals under pressure.", "built": "Multi-agent operations pipeline (LangGraph, FastAPI): natural-language incident interpretation, root-cause analysis, remediation planning with cost-optimisation logic.", "documented_result": "Résumé status: early prototype — the incident-interpretation and remediation-planning modules exist, but the end-to-end pipeline is not yet wired together; not claimed as production.", "technologies_in_human_mode": [ "Python", "LangGraph", "FastAPI" ], "categories": [ "AI Infrastructure", "Autonomous Operations" ], "demonstrates": [ "capability-ai-infra" ], "repository": "https://github.com/Sneh30/deepops", "verification": { "status": "public_repo", "qualifiers": [ "experimental" ], "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public", "caveat": "Experimental; not presented as a production system." } }, { "id": "project-esp32", "name": "ESP32-CAM Vision Suite", "one_line": "Three ESP32-CAM projects: ML dataset capture, real-time colour-tracking robot, and a 60 FPS MJPG streaming server.", "problem": "Useful computer vision on hardware with 512 KB RAM and no GPU.", "built": "Web-based ML dataset image capture; autonomous colour-tracking robot with on-device RGB565→RGB888→HSV conversion and L298N motor control; MJPEG streaming server supporting 15+ camera board variants.", "documented_result": "3 projects · 60 FPS streaming · 15+ board variants · 512 KB RAM ceiling.", "technologies_in_human_mode": [ "C", "ESP32", "Computer Vision", "HSV" ], "categories": [ "Computer Vision / Edge AI", "Embedded / Physical Systems" ], "demonstrates": [ "capability-embedded" ], "repository": "https://github.com/Sneh30/esp32-cam-vision", "verification": { "status": "public_repo", "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public", "source": "repository + résumé entry" } }, { "id": "project-catalyst", "name": "Catalyst", "one_line": "AI-powered career operating system with resume auditing, job scraping, ATS-optimized resume generation and weekly progress tracking.", "problem": "Job-search work is fragmented across tools.", "built": "Backend and application logic (Python/FastAPI), resume-audit and ATS-generation flows, job scraping, SQLite-backed tracker.", "documented_result": "No usage metrics published.", "technologies_in_human_mode": [ "Python", "FastAPI", "JavaScript", "SQLite" ], "categories": [ "Developer / Full-stack" ], "demonstrates": [ "capability-backend" ], "repository": "https://github.com/Sneh30/Catalyst", "verification": { "status": "public_repo", "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public" } }, { "id": "project-portfolio", "name": "Portfolio", "one_line": "This site: semantic HTML/CSS/JS with canvas rendering, scroll animation, live API integration and an Agent Mode record.", "problem": "Present engineering work credibly to both humans and machines.", "built": "Front-end, canvas terrain renderer, GSAP scroll choreography, GitHub/LeetCode API integration, Agent Mode record and machine-readable files.", "documented_result": "Publicly deployed at https://sneh30.github.io/portfolio/.", "technologies_in_human_mode": [ "HTML", "CSS", "JavaScript", "GitHub Pages" ], "categories": [ "Developer / Open Source" ], "demonstrates": [ "capability-backend" ], "repository": "https://github.com/Sneh30/portfolio", "verification": { "status": "public_repo", "checked": "2026-09-30", "repository_exists": true, "publicly_verifiable": true, "access": "public" } }, { "id": "project-agentos", "name": "AgentOS", "one_line": "Personal agent operating system with 6 composable layers and 250+ app integrations.", "problem": "Personal AI tooling is fragmented across interfaces, models and tools.", "built": "Per portfolio: Interface, Model Router, Simulation Engine, Memory Graph, Guard Layer and Execution Plane wired to 250+ app integrations via Composio.", "documented_result": "6 layers · 250+ app integrations (portfolio claim).", "technologies_in_human_mode": [ "TypeScript", "Next.js", "Supabase", "Composio" ], "categories": [ "Agentic AI" ], "demonstrates": [ "capability-agents" ], "repository": "https://github.com/Sneh30/AgentOS", "verification": { "status": "public_repo", "checked": "2026-10-20", "repository_exists": true, "publicly_verifiable": true, "access": "public", "note": "Public repository at https://github.com/Sneh30/AgentOS (TypeScript); reachable anonymously (HTTP 200) and its README documents the same six-layer architecture and Composio execution plane (checked 2026-10-20). The 250+ integration figure is the repository’s own count, not independently measured." } }, { "id": "project-agentic-hinge", "name": "Mobile GUI Automation Agent", "one_line": "Computer-use automation over iPhone Mirroring combining screen perception, computer vision, and GUI automation.", "problem": "Automating a mobile-only interface from the desktop requires bridging mirroring, screen perception and GUI control.", "built": "Per portfolio: a macOS agent combining computer vision with GUI automation over iPhone Mirroring; the original prototype drove a dating app (Hinge) interface to generate reply suggestions.", "documented_result": "No numeric metric published.", "technologies_in_human_mode": [ "Swift", "Computer Vision", "GUI Automation" ], "categories": [ "Computer Use", "Computer Vision / Edge AI", "Agentic AI" ], "demonstrates": [ "capability-agents" ], "repository": "https://github.com/Sneh30/Agentic-hinge", "verification": { "status": "private_available_on_request", "checked": "2026-10-20", "repository_exists": true, "publicly_verifiable": false, "access": "available_on_request", "access_note": "Private repository at https://github.com/Sneh30/Agentic-hinge. Anonymous visitors receive HTTP 404. The candidate can grant read access to recruiters and screening teams on request; until then the description is the owner's own account and is not independently verifiable.", "note": "Private repository at https://github.com/Sneh30/Agentic-hinge (Swift); its README matches the description (macOS GUI agent over iPhone Mirroring, checked 2026-10-20). Anonymous visitors receive a 404 because the repository is private; access is available on request, so its claims are not publicly verifiable." }, "aka": "Agentic-hinge (original prototype)" }, { "id": "project-pocket-typist", "name": "Pocket Typist", "one_line": "ESP32 BLE HID firmware with a Cloudflare Workers relay for remote text-input experiments.", "problem": "Getting text from any device onto any machine without installing software.", "built": "Per portfolio: ESP32 BLE HID keyboard firmware plus a Cloudflare Workers relay that pushes text to the device.", "documented_result": "No numeric metric published.", "technologies_in_human_mode": [ "C++", "ESP32", "BLE", "Cloudflare" ], "categories": [ "Embedded / Physical Systems" ], "demonstrates": [ "capability-embedded" ], "repository": "https://github.com/Sneh30/pocket-typist", "verification": { "status": "private_available_on_request", "checked": "2026-10-20", "repository_exists": true, "publicly_verifiable": false, "access": "available_on_request", "access_note": "Private repository at https://github.com/Sneh30/pocket-typist. Anonymous visitors receive HTTP 404. The candidate can grant read access to recruiters and screening teams on request; until then the description is the owner's own account and is not independently verifiable.", "note": "Private repository at https://github.com/Sneh30/pocket-typist (C++); its README matches the description (ESP32-CAM BLE HID firmware with a Cloudflare Worker relay, checked 2026-10-20). Anonymous visitors receive a 404 because the repository is private; access is available on request, so its claims are not publicly verifiable." } } ], "skills": { "note": "Reproduced exactly as displayed in Human Mode. Not expanded, upgraded, or reinterpreted for Agent Mode.", "languages": [ "Python", "Java", "SQL" ], "ai_agent_systems": [ "LangGraph", "OpenAI Codex", "Claude Code", "OpenCode", "OpenRouter", "Ollama", "Llama.cpp" ], "ai_ml": [ "Hugging Face Transformers", "LLM Inference", "Multi-Agent Systems", "Prompt Engineering", "Edge AI" ], "backend_cloud": [ "FastAPI", "PostgreSQL", "Prometheus", "REST APIs", "AWS", "Observability", "Incident Response" ], "infra_tooling": [ "Docker", "Docker Compose", "Git", "GitHub Actions", "CI/CD", "Linux", "WASM", "Metal (GPU)" ], "embedded_hardware": [ "ESP32", "Raspberry Pi", "Edge Impulse" ], "exploring": [ "Multi-Agent Systems", "Edge AI" ] }, "open_source": [ { "repository": "https://github.com/agentrhq/webcmd", "pull_request": "https://github.com/agentrhq/webcmd/pull/190", "title": "Normalize output-format parsing across builtin commands", "status": "external_validation", "status_note": "Merged upstream pull request; accepted by a maintainer outside this portfolio.", "merged_at": "2026-08-13", "commits": 16, "commits_verified": true, "tests_added": 16, "commands_fixed": "15+", "upstream_stars": 2620, "upstream_stars_checked": "2026-09-30", "author": "Sneh30", "why_it_matters": "Externally reviewed and accepted code in a project the candidate does not own." } ], "achievements": [ "Merged PR #190 into agentrhq/webcmd (16 commits, 16 tests, 15+ commands unified)", "~40% reduction in infrastructure incidents and average recovery time across a 400+ node network", "ABS firmware developed, deployed and validated through track testing, SAE India scrutineering and competition", "Up to 80–85% LLM inference cost reduction in PromptOS Economy mode (per résumé)", "732+ GitHub contributions (portfolio figure, self-published) and 13 public repositories (GitHub API, 2026-09-30)", "GPA 8.0/10, B.E. Robotics and Artificial Intelligence (per résumé)" ], "roles": [ { "id": "role-ai-engineer", "role": "AI Engineer", "support": "strongly_supported", "aliases": [ "Applied AI Engineer", "AI Software Engineer" ], "evidence": [ "capability-agents", "capability-multagent", "capability-llm" ], "projects": [ "project-opencode-cua", "project-orchestra", "project-deepops", "project-promptos" ] }, { "id": "role-agentic-ai-engineer", "role": "Agentic AI Engineer", "support": "strongly_supported", "aliases": [ "Agentic Systems Engineer", "LLM Agent Engineer" ], "evidence": [ "capability-agents", "capability-multagent" ], "projects": [ "project-opencode-cua", "project-orchestra", "project-agentos", "project-agentic-hinge" ] }, { "id": "role-ai-infrastructure-engineer", "role": "AI Infrastructure Engineer", "support": "supported", "basis": { "kinds": [ "project", "engagement" ] }, "aliases": [ "AI Platform Engineer", "AI Systems Engineer" ], "evidence": [ "capability-ai-infra", "capability-ops" ], "projects": [ "project-deepops", "project-orchestra" ], "experience": [ "experience-infrastructure" ] }, { "id": "role-llm-engineer", "role": "LLM Engineer", "support": "supported", "aliases": [ "LLM Applications Engineer", "Inference Engineer" ], "evidence": [ "capability-llm" ], "projects": [ "project-promptos", "project-orchestra" ], "caveat": "No model training, fine-tuning, or research claimed; evidence is systems- and cost-oriented." }, { "id": "role-autonomous-systems-engineer", "role": "Autonomous Systems Engineer", "support": "supported", "aliases": [ "Autonomous Systems Software Engineer" ], "evidence": [ "capability-embedded", "capability-safety", "capability-ai-infra" ], "projects": [ "project-esp32", "project-deepops" ], "experience": [ "experience-team-stratos" ] }, { "id": "role-ai-software-systems-engineer", "role": "AI/Software Systems Engineer", "support": "supported", "aliases": [ "Full-Stack AI Engineer" ], "evidence": [ "capability-backend", "capability-oss" ], "projects": [ "project-orchestra", "project-promptos", "project-catalyst", "project-portfolio" ] }, { "id": "role-ml-engineer", "role": "ML Engineer", "support": "partially_supported", "evidence": [ "capability-embedded" ], "projects": [ "project-esp32" ], "caveat": "The portfolio does not document model training, evaluation benchmarks, or deployed ML services; Human Mode lists Multi-Agent Systems and Edge AI under 'Exploring'." }, { "id": "role-software-engineer", "role": "Software Engineer", "support": "supported", "aliases": [ "Software Developer" ], "evidence": [ "capability-backend", "capability-oss" ], "projects": [ "project-orchestra", "project-catalyst", "project-portfolio", "project-webcmd" ], "experience": [ "experience-team-stratos" ] }, { "id": "role-backend-engineer", "role": "Backend Engineer", "support": "supported", "aliases": [ "Backend Developer", "API Engineer" ], "evidence": [ "capability-backend" ], "projects": [ "project-orchestra", "project-catalyst" ] }, { "id": "role-python-developer", "role": "Python Developer", "support": "supported", "basis": { "note": "Python listed first under Languages in Human Mode; all four projects list Python in Human Mode" }, "aliases": [ "Python Engineer", "Python Software Engineer" ], "evidence": [ "capability-backend", "capability-agents" ], "projects": [ "project-opencode-cua", "project-orchestra", "project-deepops", "project-catalyst" ] }, { "id": "role-devops-sre-engineer", "role": "DevOps / SRE Engineer", "support": "supported", "basis": { "kinds": [ "project", "engagement" ] }, "aliases": [ "Site Reliability Engineer", "DevOps Engineer" ], "evidence": [ "capability-ai-infra", "capability-ops" ], "projects": [ "project-deepops", "project-orchestra" ], "experience": [ "experience-infrastructure" ], "caveat": "No Kubernetes experience is claimed — Kubernetes is not listed in Human Mode skills. The evidenced tooling is Docker, Docker Compose, GitHub Actions CI/CD, Linux, Prometheus, AWS, and incident response." }, { "id": "role-automation-engineer", "role": "Automation Engineer", "support": "supported", "aliases": [ "Infrastructure Automation Engineer", "Automation Developer" ], "evidence": [ "capability-ai-infra", "capability-agents", "capability-oss" ], "projects": [ "project-deepops", "project-webcmd", "project-agentic-hinge" ], "caveat": "The automation evidence is infrastructure remediation, CLI behaviour, and desktop/mobile GUI control. No RPA platform or test-automation framework is claimed." }, { "id": "role-embedded-systems-engineer", "role": "Embedded Systems Engineer", "support": "supported", "aliases": [ "Embedded Software Engineer", "Firmware Engineer" ], "evidence": [ "capability-embedded", "capability-safety" ], "projects": [ "project-esp32", "project-pocket-typist" ], "experience": [ "experience-team-stratos" ] }, { "id": "role-robotics-engineer", "role": "Robotics Engineer", "support": "supported", "aliases": [ "Robotics Software Engineer" ], "evidence": [ "capability-embedded", "capability-safety" ], "projects": [ "project-esp32", "project-pocket-typist" ], "experience": [ "experience-team-stratos" ], "caveat": "No ROS, manipulator, or control-system tooling is listed in Human Mode skills. The robotics evidence is ESP32 perception and an autonomous colour-tracking robot, BLE device firmware, and safety-critical vehicle firmware, alongside the B.E. in Robotics and Artificial Intelligence." }, { "id": "role-computer-vision-engineer", "role": "Computer Vision Engineer", "support": "supported", "aliases": [ "Edge Computer Vision Engineer", "Perception Engineer" ], "evidence": [ "capability-embedded" ], "projects": [ "project-esp32", "project-agentic-hinge" ], "caveat": "No large-scale dataset, benchmark, or published model weights are claimed; the vision work is edge and screen-perception driven." } ], "support_levels": { "note": "How directly the published evidence supports the role. No fit scores, percentages, or rankings are computed.", "values": [ "strongly_supported", "supported", "partially_supported" ], "definitions": { "strongly_supported": "Several published artefacts in this area, including at least one flagship project with an independently verifiable repository.", "supported": "At least one published artefact or one documented engagement addresses this role directly.", "partially_supported": "Adjacent evidence exists, but a stated part of the role is explicitly not claimed (see `caveat`)." } }, "job_search": { "open_to_work": true, "source": "Open to work (as displayed on the portfolio)", "engagement_types": [ "internship", "full_time" ], "levels": [ "intern", "entry_level" ], "roles_ref": "candidate.json#/roles", "note": "Levels follow current standing (B.E. 2023-2027) and the portfolio's open-to-work status. Every listed role is sought at those levels; no prior internship, prior full-time employment, or qualification beyond the two documented engagements and the published B.E. record is claimed." }, "evidence_labels": { "note": "Qualitative labels only — no numeric evidence scores, fit percentages, or rankings are computed. `status` always holds exactly one value from `values`; `qualifiers` holds any additional ones.", "values": [ "public_repo", "private_available_on_request", "external_validation", "documented_experience", "self_reported", "experimental" ], "definitions": { "public_repo": "Repository exists and is publicly readable without authentication (HTTP 200 on 2026-10-20), so the claim can be checked by anyone.", "private_available_on_request": "Repository exists but is private: anonymous visitors get HTTP 404. The candidate can provide access to recruiters on request, so the work is real and inspectable on request, but it is not independently verifiable without that access.", "external_validation": "Reviewed and accepted by someone outside this portfolio (for example a merged upstream pull request).", "documented_experience": "Described in the résumé published with this portfolio; not independently verifiable here.", "self_reported": "A metric stated by the candidate (for example the PromptOS cost reduction); no raw data is published.", "experimental": "Early prototype or active development; not a claimed production deployment." } }, "limitations": [ "Employer name for the Aug 2026 infrastructure engagement is not disclosed; no raw incident dataset is published.", "AgentOS resolves publicly, while Agentic-hinge and Pocket Typist are private repositories (checked 2026-10-20) — anonymous visitors receive a 404 and access is available on request.", "The 732+ contributions figure is self-published on the portfolio and was not independently verified.", "PromptOS 80–85% and ESP32 512 KB / 15+ board figures come from the résumé PDF, not independent measurement.", "DeepOps is described as an early prototype (modules exist, end-to-end pipeline not yet connected); it is not presented as a production system.", "Team Stratos is a team project; the code is not published.", "No published research, peer-reviewed papers, certifications, awards, or employer-scale production deployments are claimed.", "The candidate's own GitHub repositories currently show 0 stars; the 2.6k star figure belongs to the upstream webcmd project." ] }