{"id":3763682,"url":"https://alion.io/job/hcltech-senior-developer-dpo-2","title":"Senior Developer (DPO)","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":"senior","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":null,"experience_years_min":3,"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":"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":"2026-11-07T14:54:34Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary\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\n. RAG 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\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\nPreferred 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.\n5. Key Performance Indicators (KPIs) & Success Benchmarks\nSuccess and engineering performance in this role will be measured against the following technical benchmarks:\n1. Retrieval Accuracy: RAG Faithfulness & Retrieval Relevance: Achieving ≥90% ground-truth accuracy and minimal hallucination scores on benchmark evaluation datasets.\n 2. Low Latency: Inference Latency SLAs: Maintaining p95 streaming Time-to-First-Token (TTFT) < 800ms and total request latency within defined application budgets.\n3. Security & Safety: System Robustness & Guardrails: Zero critical prompt injection bypasses or unauthorized data leakage incidents in production environments.\n4. Engineering Quality: Code Quality & Test Coverage: Delivering modular, well-documented code with ≥80% automated unit/integration test coverage across API endpoints.\n 5. Delivery Velocity: Sprint Execution Velocity: Consistently completing committed sprint stories and technical spikes on schedule with minimal defect slippage.\nOther Requirements","description_format":"text","description_chars":6710,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"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":2,"expected_fill_days":28,"reasons":["conf:3","velocity","win:early","comp:brand"],"computed_at":"2026-11-07T05:47:08Z"},"pay":null,"html_url":"https://alion.io/job/hcltech-senior-developer-dpo-2","json_url":"https://alion.io/job/hcltech-senior-developer-dpo-2.json","meta":{"generated_at":"2026-11-07T15:42:22Z","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":21496,"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-senior-developer-dpo-2"},{"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-senior-developer-dpo-2"},{"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-senior-developer-dpo-2"},{"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-senior-developer-dpo-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhcltech-senior-developer-dpo-2"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3763682}},{"tool":"search_jobs","arguments":{"company":"hcltech.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3763682},"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. 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