Sirjan Singh, AI/ML Engineer

B.Tech in Computer Science and Engineering (AI and Data Science), The LNM Institute of Information Technology, graduating May 2027. CGPA 7.58 / 10. Based in India.

Highlights

1st Place, Arize Track (Google Cloud Rapid Agent Hackathon): First place in the Arize track of Google Cloud's Rapid Agent Hackathon. The hackathon had 14,000+ participants worldwide.
15% → 60% Agent survival rate, 4× higher (SurviveCity): In SurviveCity, a multi-agent survival game, agents' average survival rate went from 15% to 60% after fine-tuning Qwen2.5-3B with GRPO and LoRA: four times better. Three reward-hacking exploits were found and closed on the way.
+30% Better at finding the right code (Contextual, retrieval F1): Contextual answers questions about a codebase. Combining keyword search, meaning-based search and a reranker found the right code 30% better (by F1 score, which rewards finding the right pieces and punishes pulling in wrong ones) than meaning-based search alone.
105 / 105 Negotiations the guard held (Vendable): Vendable was tested against 105 recorded negotiations with persistent buyers trying to talk the AI into a lower price. Prompt-only rules gave way; a 7-line deterministic guard held every median at the published entitlement.
90% Overlap with real rooftops (Solar research, IoU 0.9016): The model finds rooftops in satellite images to estimate solar energy potential. Its predicted rooftop outlines overlapped the real ones by 90% (IoU 0.9016, where 1.0 is a perfect match), using a two-stage U-Net / ResNet-34.
Top 100 OpenEnv Hackathon (Meta × PyTorch × Scaler): Finished in the top 100 of the Meta × PyTorch × Scaler OpenEnv Hackathon, with work on Qwen2.5-3B, GRPO and LoRA.

Experience

AI & Full Stack Intern, AARM Health Inc. (APR to SEP 2026, Remote · California, USA)

Built and shipped Maya, an LLM-driven medical assistant using intent classification and an agentic pipeline. Full-stack implementation across Node/Express, PostgreSQL, React, AWS, and pm2, plus a write-path safety layer controlling what the model may commit.

Visiting Research Intern, King's College London (JUN to JUL 2026, Research)

Worked on multimodal Vision Transformers and CLIP for skin-lesion classification using clinical metadata, including fusion, text baselines, and robustness studies.

Software Development Trainee, Cloudsprint Technologies / Trovex.ai (JUN to JUL 2025, Software engineering)

Worked on React.js components, REST API integrations, and production applications.

Research Intern, LNMIIT LUSIP (SUMMER 2024, Dr. Preety Singh)

Built a Selenium + ETL pipeline for a dataset of 6,000+ Reddit pairs and a neural sentiment classifier.

Projects

Cassandra: Autonomous LLM Agent Supervision

An 8-stage autonomous meta-agent that supervises production LLM agents through Phoenix telemetry, detecting hallucinations, prompt drift, and tool-call failures in under 10 seconds via LLM-as-judge.

Result: Failure detection in under 10 seconds; 5 supervision tools exposed through cassandra-mcp.
Stack: Python, Google ADK, Gemini, Arize Phoenix MCP
Shows: Evaluation, Observability, Regression-safe patching, MCP tool authoring
Problem: Agent failures are difficult to reproduce, measure, and repair without introducing regressions.
Approach: Inspect Phoenix traces, classify failures, generate adversarial tests, patch prompts, compare pass-rate, cost, and latency, then replay the original failure.
Experiments: Baseline and patched prompts are evaluated against synthesized adversarial cases before a change is accepted.
What failed: A plausible patch can improve one example while degrading broader behavior.
Decision: Treat prompt changes like code changes: test, compare, replay, and only then resolve.

Vendable: Agent-Transactable Commerce

A commerce system exploring where agent negotiation must stop and deterministic entitlement enforcement must begin.

Result: Across 105 recorded negotiations, a 7-line statutory guard held every median on the published entitlement.
Stack: Python, MCP, Razorpay
Shows: Deterministic safety, Idempotent replay, Audit chain, Red-team suite
Problem: Prompt rules can be persuaded away under persistent negotiation, even when the business rule is non-negotiable.
Approach: Keep the model in the conversational layer while enforcing entitlements in a small deterministic guard with no model call.
Experiments: Compared system-prompt rules against a statutory guard across 105 recorded negotiation attempts.
What failed: Pure buyer persistence defeated prompt-only rules.
Decision: Move critical invariants out of probabilistic inference and into deterministic enforcement.

AgentOS: Agent Orchestration & Observability

A Node API and WebSocket server with a React interface spanning roughly 36 TypeScript source files.

Result: A real-time interface for agent runs, approvals, skill costs, and outcomes.
Stack: TypeScript, Node.js, React, WebSockets
Shows: Human-in-the-loop gating, Per-skill cost, Success observability, Run visualization
Problem: Multi-step agents hide consequential tool calls and make cost or failure difficult to attribute.
Approach: Stream execution events from a Node server into a React console and gate sensitive tools behind human approval.
Experiments: Instrumented skill-level cost and success signals across deterministic mocked traces in this portfolio walkthrough.
What failed: A run-level success label can hide which skill or tool caused cost growth or failure.
Decision: Make observability granular to skills and tools, not only the final response.

SurviveCity: OpenEnv Multi-Agent RL

An OpenEnv-compliant multi-agent survival environment with hidden roles, 14 actions, and 15 reward rubrics. Qwen2.5-3B was fine-tuned with GRPO and LoRA on a V100 DGX cluster.

Result: Agent survival rate went from 15% to 60%, four times higher; 3 reward-hacking exploits were closed.
Stack: PyTorch, TRL, GRPO, LoRA, Qwen2.5-3B
Shows: Multi-agent RL, Reward-hack analysis, Post-training, Custom rollout
Problem: Agents optimized the rubric in ways that failed the actual survival objective.
Approach: Build a custom multi-action rollout and patch specific reward rubrics after reproducing exploits.
Experiments: Tracked survival, actions, hidden roles, and 15 rubrics through GRPO + LoRA training.
What failed: The apparent policy-collapse signal was actually starvation.
Decision: Debug the environment and reward signals before blaming the policy.

Case study

Contextual: Repo-Aware RAG Assistant

Hybrid retrieval over 10k+ code chunks combining BM25, FAISS, and cross-encoder reranking with sub-second retrieval.

Result: 30% retrieval F1 improvement versus a semantic-only baseline.
Stack: Python, FAISS, BM25, Cross-Encoder, Gemini
Shows: Hybrid retrieval, Evaluation harness, Reranking, Three shipped interfaces
Problem: Semantic similarity alone misses exact identifiers and repository-specific language.
Approach: Merge lexical and dense candidates, rerank with a cross-encoder, then evaluate the retrieved context.
Experiments: Measured precision, recall, F1, keyword hit-rate, and latency against a semantic-only baseline.
What failed: Dense retrieval underweighted exact code symbols and project vocabulary.
Decision: Use complementary retrievers and evaluate retrieval independently from generation.

Case study

claude-kit: Agentic Development Toolkit

Open-source agent skills and guardrails scoped from counted telemetry rather than opinion.

Result: Reusable agent tooling for other developers, released under MIT.
Stack: Node.js, Python, Playwright
Shows: Developer tooling, Open source, Telemetry-scoped rules, MIT licensed
Problem: Agent coding rules often accumulate as untested opinions rather than responses to measured failure.
Approach: Use counted telemetry to identify recurring failure modes and scope focused skills and guardrails.
Experiments: Observe behavior, count failures, then keep only rules that answer an evidenced pattern.
What failed: Overbroad rules can make agents less effective while appearing safer.
Decision: Prefer small, evidence-backed constraints over sprawling instruction sets.

Skills

Models & post-training: PyTorch, Hugging Face, TRL, PEFT, GRPO, LoRA, Vision Transformers, CLIP
Agents & evaluation: LLM-as-judge, Arize Phoenix, MCP, Google ADK, Tool calling, Agent orchestration
Retrieval & data: RAG, FAISS, BM25, Cross-encoder reranking, SQL
Systems & product: Python, TypeScript, C/C++, FastAPI, React, Node.js, WebSockets, AWS, GCP, Docker, Playwright, Git

Achievements

Meta × PyTorch × Scaler OpenEnv Hackathon (TOP 100): Qwen2.5-3B · GRPO · LoRA
ACM Coding Competition (1ST PLACE TEAM): Four rounds of logic and CTF-style challenges
CTRL+SPACE National Hackathon (ORGANIZER): 1,600+ participants · Rs. 1 Lakh prize pool
Competitive Programming (CP SIGNAL): Codeforces Specialist · 1464 / LeetCode Knight · 2003 / Problem setter

Licence

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