{"id":3763679,"url":"https://alion.io/job/hcltech-sr-development-lead","title":"Sr. Development Lead","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Anthropic","optional":false},{"name":"Asyncio","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Chain-of-Thought","optional":false},{"name":"Chroma","optional":false},{"name":"Cohere SDK","optional":false},{"name":"DeepEval","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Hugging Face","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Instructor","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mistral","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NeMo Guardrails","optional":false},{"name":"Ollama","optional":false},{"name":"OpenAI","optional":false},{"name":"Outlines","optional":false},{"name":"PEFT","optional":false},{"name":"pgvector","optional":false},{"name":"Phi","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"Rest API","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Transformers","optional":false},{"name":"TruLens","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"WebSockets","optional":false},{"name":"Weights & Biases","optional":false},{"name":"Agile","optional":true},{"name":"AWQ","optional":true},{"name":"GGUF","optional":true},{"name":"GitHub","optional":true},{"name":"GPTQ","optional":true},{"name":"Hallucination","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Quantization","optional":true},{"name":"Scrum","optional":true},{"name":"TensorRT","optional":true},{"name":"TGI","optional":true}],"status":"live","first_seen_at":"2026-11-04T19:18:24Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-07T02:30:54Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary\n\nRole Title:\n\nGenerative AI Engineer / Applied AI Developer\n\nExperience Required:\n\nMinimum 5+ Years in Software / Machine Learning (with 1.5+ years hands-on in LLMs & GenAI)\n\nCore Tech Stack:\n\nPython, PyTorch, LangChain, LlamaIndex, Vector DBs, FastAPI, Docker, Cloud AI (AWS/Azure)\n\nTarget Work Model:\n\nHybrid (Enterprise Office / Remote flexibility available)\n\nEmployment Type:\n\nFull-Time, Permanent Position\n\nWe are seeking a talented, technically passionate Generative AI Engineer with 3+ years of professional software engineering and applied AI/ML experience to design, build, and deploy production-grade LLM applications. In this hands-on engineering role, you will turn the latest advancements in generative artificial intelligence into resilient, scalable business solutions.\nYou will specialize in constructing Advanced Retrieval-Augmented Generation (RAG) pipelines, engineering autonomous agents with deterministic tool-calling capabilities, optimizing prompt workflows, experimenting with parameter-efficient model adaptation (LoRA/QLoRA), and serving high-performance inference APIs via containerized microservices hosted on AWS or Microsoft Azure.\nKey Responsibilities\nRAG Architecture & Vector Search Engineering\nRAG Pipeline Development: Design and implement end-to-end RAG pipelines incorporating multi-modal document parsing, recursive text splitting, and semantic chunking.\n Vector Store Management: Integrate, optimize, and benchmark vector databases (Pinecone, Qdrant, Chroma, Weaviate, pgvector, Azure AI Search) for sub-second similarity search.\n Hybrid Search & Re-ranking: Implement hybrid retrieval combining dense vector embeddings with sparse lexical keyword matching (BM25) and cross-encoder re-ranking models (Cohere Rerank, BGE).\nContext Optimization: Engineer contextual compression, parent-document retrieval, and metadata filtering schemas to eliminate context noise and hallucinations.\nB. Agentic Workflows & Multi-Tool Integration\nAgentic Frameworks: Build stateful multi-agent workflows and autonomous task execution loops using LangChain, LangGraph, LlamaIndex, or AutoGen.\nTool Calling & Action APIs: Develop robust, type-safe function-calling and tool-use interfaces connecting LLMs to external REST APIs, relational databases, and enterprise systems.\nLoop Safeguards & Routing: Implement plan-and-solve agent patterns, multi-step reflection loops, and fallback error-handling to prevent infinite agent recursion.\nC. Prompt Engineering, Output Validation & Fine-Tuning\n Prompt Engineering: Design and test systematic prompt templates utilizing Chain-of-Thought (CoT), Few-Shot prompting, ReAct patterns, and directional stimulus prompting.\nStructured Output Parsing: Enforce deterministic, validated output structures using Pydantic, Instructor, Guidance, or Outlines to guarantee reliable downstream ingestion.\n Model Adaptation: Conduct Parameter-Efficient Fine-Tuning (PEFT, LoRA, QLoRA) on open-weights models (Llama 3, Mistral, Phi-3/4) for domain-specific tasks.\nD. Backend API & Inference Microservices\n Inference APIs (FastAPI): Develop high-throughput asynchronous backend microservices using Python (FastAPI), managing connection pooling, concurrency, and rate limiting.\n Streaming Latency: Implement low-latency streaming inference pipelines utilizing Server-Sent Events (SSE) and WebSockets for conversational user interfaces.\n Inference Engines: Optimize inference throughput utilizing modern serving engines like vLLM, TensorRT-LLM, or Ollama, minimizing Time-to-First-Token (TTFT).\nContainerized Deployment: Containerize applications with Docker and configure deployment manifests for Kubernetes, AWS ECS/EKS, or Azure Container Apps.\nE. Evaluation, Guardrails & LLMOps Observability\n Automated Model Evaluation: Establish automated evaluation benchmarks utilizing synthetic test datasets and evaluation harnesses (Ragas, TruLens, DeepEval) to quantify faithfulness and recall.\n AI Guardrails & Safety: Implement enterprise guardrails (NeMo Guardrails, Llama Guard, Azure Content Safety) to proactively mitigate prompt injection, jailbreaks, and toxicity.\nObservability & Tracing: Integrate comprehensive LLM tracing, prompt versioning, and cost monitoring using tools like Langfuse, Phoenix, or Weights & Biases.\nSkill Requirements\nRequired Technical Skills & Qualifications\nTotal Experience: Minimum 3+ years of professional software engineering / data science experience, with at least 1.5+ years of dedicated hands-on GenAI / LLM development.\nEducation: Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or equivalent technical field.\n Python & ML Foundations: Advanced Python programming skills (asyncio, type hints, Pydantic, pytest) and deep familiarity with PyTorch or Hugging Face Transformers.\n GenAI Frameworks: Extensive hands-on experience with LangChain, LangGraph, LlamaIndex, and commercial/open foundation model APIs (OpenAI, Anthropic, Bedrock).\n Vector Search & RAG: Proven track record building and deploying production RAG architectures using vector databases (Pinecone, Chroma, Qdrant, pgvector).\nBackend & Containers: Hands-on proficiency developing asynchronous REST APIs using FastAPI, Docker containerization, and Git-based collaborative workflows.\nCore Competencies: Strong analytical problem-solving skills, solid understanding of transformer architecture mechanics, and proactive technical curiosity.\n4. Preferred Qualifications & Industry Certifications\n Inference Optimization: Hands-on experience with model quantization (GGUF, AWQ, GPTQ) and high-throughput inference serving engines (vLLM, TGI, TensorRT-LLM).\n Cloud Certifications: Cloud AI certifications: AWS Certified AI Practitioner / ML Specialty, Microsoft Certified: Azure AI Engineer Associate (AI-102), or Azure AI Fundamentals.\nOpen Source & Portfolio: Demonstrated contributions to open-source GenAI projects, LangChain/LlamaIndex community plugins, or active technical blog/GitHub repository.\nAgile Collaboration: Experience working in Agile/Scrum sprint environments collaborating with Product Managers, UX Designers, and Frontend Developers.\nOther Requirements\n. Candidate Evaluation Matrix (For Technical Interview Panels)\nTechnical interviewers should evaluate candidates across the following five core technical engineering competencies:\n\nEvaluation Area\n\nCompetency Focus\n\nKey Technical Signals\n\nRAG Architecture & Retrieval\n\nChunking strategies, vector search, hybrid retrieval, and re-ranking\n\nExplains concrete trade-offs between chunk sizes; demonstrates practical experience with hybrid search (BM25 + vector) and cross-encoders to improve recall.\n\nAgentic Loops & Tool Calling\n\nFunction calling, stateful graphs, and error recovery mechanisms\n\nCan code and debug an agent with deterministic tool use; understands state management in LangGraph; handles schema parsing errors gracefully.\n\nPrompt Engineering & Parsing\n\nFew-shot prompting, structured JSON schema validation (Pydantic)\n\nDemonstrates advanced prompting techniques (ReAct, CoT); knows how to force LLMs to emit valid JSON schemas reliably using Instructor or Outlines.\n\nBackend & Microservice APIs\n\nFastAPI, asynchronous streaming, connection pooling, and Docker\n\nComfortably writes async Python endpoints streaming tokens via SSE; explains how to containerize an application and manage GPU/CPU resource constraints.\n\nEvaluation & LLMOps Guardrails\n\nSynthetic benchmarking, hallucination metrics, and guardrail layers\n\nArticulates how to evaluate an LLM app using Ragas or TruLens; explains how NeMo Guardrails or regex/semantic filters prevent prompt injections.","description_format":"text","description_chars":7651,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"India","iso":"IN","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Cybersecurity","Professional Services","Science & Engineering","Systems Integrators"],"lifecycle":[{"event":"open","at":"2026-11-04T19:18:24Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":1,"expected_fill_days":20,"reasons":["conf:0","velocity","win:early","comp:brand"],"computed_at":"2026-11-06T05:58:05Z"},"pay":null,"html_url":"https://alion.io/job/hcltech-sr-development-lead","json_url":"https://alion.io/job/hcltech-sr-development-lead.json","meta":{"generated_at":"2026-11-07T03:14:03Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":7768,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-08T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":225},"rest":"https://alion.io/mcp/rest/get_company?id=225"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fhcltech-sr-development-lead"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fhcltech-sr-development-lead"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fhcltech-sr-development-lead"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/hcltech-sr-development-lead\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhcltech-sr-development-lead"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3763679}},{"tool":"search_jobs","arguments":{"company":"hcltech.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3763679},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}