AI/ML Tech Lead

Role Overview :

We are seeking a high-ownership, hands-on AI / LLM Engineering Tech Lead to drive the technical execution of our core AI platforms. In this role, you will bridge deep architectural vision with direct code-level execution. You will lead an agile engineering pod building enterprise-grade Agentic Workflows, Knowledge Graphs, and AI Security Safeguards.

Key Responsibilities :

- Lead an engineering pod (AI/ML Engineers, Full-Stack Developers, and DevOps) to ship low-latency, production-ready AI features.

- Translate high-level blueprints into actionable technical specifications, clean codebases, and sprint backlogs.

- Enforce engineering excellence through code reviews, automated CI/CD testing protocols, and robust error-handling standards.

- Architect & Code : Build multi-modal LLM workflows and autonomous agentic systems using modern orchestration frameworks.

- Knowledge Layer Integration : Implement knowledge graphs, dynamic ontologies, and advanced vector retrieval strategies (Hybrid Search, Graph RAG, Re-ranking).

- AI Security & Guardrails : Deploy active safeguards against prompt injection, model jailbreaks, hallucination, and data leakage.

- LLM Ops : Build automated pipelines for continuous model evaluation (e.g. RAGAS, TruLens), dynamic prompt versioning, and latency tracking.

- Cost & Throughput Optimization : Optimize token consumption, context window management, caching, and model inference costs.

- Observability : Monitor model drift, data distribution shifts, and edge-case execution in live enterprise production environments.

- Collaborate closely with Product Managers, Solution Architects, and client teams to resolve complex edge cases and accelerate feature delivery.

- Serve as a technical mentor, elevating team execution standards and unblocking complex algorithmic or system challenges daily.

Required Qualifications :

- Experience : 5+ years of core software engineering experience, including 3+ years specifically architecting and delivering AI/ML or LLM-based products into production.

- Leadership : Proven track record leading agile pods, conducting technical design reviews, and mentoring developers.

- Education : Master's in Computer Science, Data Science, AI, or equivalent practical experience demonstrated through shipped products or open-source contributions and professional certifications.

Tech Stack :

- Languages : Python (FastAPI, PyDantic, Asyncio), TypeScript, Go, or Java.

- Agentic Frameworks & AI Stack : LangGraph, AutoGen, CrewAI, LangChain, LlamaIndex, PyTorch, Hugging Face, and major LLM Provider APIs.

- Vector Engines & Knowledge Graphs : Qdrant, Pinecone, Milvus, Weaviate, Neo4j, RDF/Ontologies.

- AI Security & Guardrails : Adversarial prompt testing, red teaming concepts, and guardrail implementation.

- MLOps & Infra : Docker, Kubernetes, GitHub Actions, MLflow, Weights & Biases, and serverless AI infrastructure on AWS/GCP/Azure.

Soft Skills :

- Strong technical articulation and communication skills to engage with technical stakeholders, understand requirements, and present engineering solutions cleanly.