{"id":1746297,"url":"https://alion.io/job/connect-techtalent-technical-lead","title":"Technical Lead","company":{"id":3875545,"name":"Connect Tech+Talent","domain":"connecttechtalent.com","url":"https://alion.io/company/connect-tech-plus-talent","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":54},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Neo4j","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TruLens","optional":false},{"name":"Weights & Biases","optional":false},{"name":"Asyncio","optional":true},{"name":"AutoGen","optional":true},{"name":"CrewAI","optional":true},{"name":"FastAPI","optional":true},{"name":"Hugging Face","optional":true},{"name":"Java","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Milvus","optional":true},{"name":"Pinecone","optional":true},{"name":"Python","optional":true},{"name":"Qdrant","optional":true},{"name":"Red Teaming","optional":true},{"name":"TypeScript","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-03T05:25:51Z","employer_posted_date":null,"last_verified_at":"2026-10-03T05:25:51Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"AI/ML Tech Lead\n\nRole Overview : \n\nWe 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.\n\nKey Responsibilities : \n\n- Lead an engineering pod (AI/ML Engineers, Full-Stack Developers, and DevOps) to ship low-latency, production-ready AI features.\n\n- Translate high-level blueprints into actionable technical specifications, clean codebases, and sprint backlogs.\n\n- Enforce engineering excellence through code reviews, automated CI/CD testing protocols, and robust error-handling standards.\n\n- Architect & Code : Build multi-modal LLM workflows and autonomous agentic systems using modern orchestration frameworks.\n\n- Knowledge Layer Integration : Implement knowledge graphs, dynamic ontologies, and advanced vector retrieval strategies (Hybrid Search, Graph RAG, Re-ranking).\n\n- AI Security & Guardrails : Deploy active safeguards against prompt injection, model jailbreaks, hallucination, and data leakage.\n\n- LLM Ops : Build automated pipelines for continuous model evaluation (e.g. RAGAS, TruLens), dynamic prompt versioning, and latency tracking.\n\n- Cost & Throughput Optimization : Optimize token consumption, context window management, caching, and model inference costs.\n\n- Observability : Monitor model drift, data distribution shifts, and edge-case execution in live enterprise production environments.\n\n- Collaborate closely with Product Managers, Solution Architects, and client teams to resolve complex edge cases and accelerate feature delivery.\n\n- Serve as a technical mentor, elevating team execution standards and unblocking complex algorithmic or system challenges daily.\n\nRequired Qualifications : \n\n- Experience : 5+ years of core software engineering experience, including 3+ years specifically architecting and delivering AI/ML or LLM-based products into production.\n\n- Leadership : Proven track record leading agile pods, conducting technical design reviews, and mentoring developers.\n\n- Education : Master's in Computer Science, Data Science, AI, or equivalent practical experience demonstrated through shipped products or open-source contributions and professional certifications.\n\nTech Stack : \n\n- Languages : Python (FastAPI, PyDantic, Asyncio), TypeScript, Go, or Java.\n\n- Agentic Frameworks & AI Stack : LangGraph, AutoGen, CrewAI, LangChain, LlamaIndex, PyTorch, Hugging Face, and major LLM Provider APIs.\n\n- Vector Engines & Knowledge Graphs : Qdrant, Pinecone, Milvus, Weaviate, Neo4j, RDF/Ontologies.\n\n- AI Security & Guardrails : Adversarial prompt testing, red teaming concepts, and guardrail implementation.\n\n- MLOps & Infra : Docker, Kubernetes, GitHub Actions, MLflow, Weights & Biases, and serverless AI infrastructure on AWS/GCP/Azure.\n\nSoft Skills : \n\n- Strong technical articulation and communication skills to engage with technical stakeholders, understand requirements, and present engineering solutions cleanly.\nSkills\nPython, LangChain, LangGraph, Knowledge 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