{"id":3179043,"url":"https://alion.io/job/opkey-lead-llm-engineer","title":"Lead LLM Engineer","company":{"id":2131879,"name":"Opkey","domain":"opkey.com","url":"https://alion.io/company/opkey","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":64000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":44},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hallucination","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"PEFT","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Qdrant","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"SFT","optional":false},{"name":"Transformers","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"Knowledge Graph","optional":true},{"name":"LangChain","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"OCR","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-23T20:36:00Z","employer_posted_date":null,"last_verified_at":"2026-10-23T20:36:00Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"We are looking for a Lead LLM Engineer to drive the design, architecture, and deployment of production-grade Generative AI systems. This is a highly hands-on role (approximately 70% Individual Contributor, 30% Technical Leadershipwhere you will architect scalable AI platforms while mentoring engineers and defining technical direction. You will lead the development of enterprise-grade LLM applications, Agentic AI workflows, Retrieval-Augmented Generation (RAG) systems, and multimodal AI solutions that power real-world products at scale.\n\nResponsibilities: \nDesign, build, and deploy production-grade LLM applications using state-of-the-art foundation models and agentic AI frameworks.\nArchitect scalable Retrieval-Augmented Generation (RAG) and GraphRAG systems with hybrid retrieval, reranking, and evaluation pipelines.\nLead the development of multi-agent AI workflows using frameworks such as LangGraph, AutoGen, CrewAI, or similar.\nFine-tune and optimize LLMs/SLMs using LoRA, QLoRA, PEFT, distillation, quantization, and inference optimization techniques.\nBuild robust evaluation frameworks covering hallucination detection, factuality, latency, cost, and response quality.\nDevelop multimodal AI solutions combining text, images, documents, and structured enterprise data.\nCollaborate with Product, Engineering, and Design teams to integrate AI capabilities into production products.\nMentor junior engineers, conduct architecture reviews, establish engineering best practices, and provide technical leadership across the AI team.\nDrive AI platform architecture, observability, experimentation, and MLOps best practices.\nStay current with advancements in LLMs, Agentic AI, reasoning models, multimodal AI, and enterprise AI infrastructure.\n\nRequirements: \n5-8 years of experience in Machine Learning, NLP, Deep Learning, or Applied AI.\n2+ years of hands-on experience building production LLM or Generative AI applications.\nProven experience leading technical initiatives or mentoring engineering teams.\n\nTechnical Skills: \nStrong expertise in Large Language Models (LLMs), Transformer architectures, embeddings, and prompt engineering.\nHands-on experience designing production RAG/GraphRAG systems.\nExperience building Agentic AI workflows using LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, or equivalent.\nStrong knowledge of vector databases such as Pinecone, FAISS, Qdrant, ChromaDB, Weaviate, or Milvus.\nExperience fine-tuning LLMs using LoRA, QLoRA, PEFT, instruction tuning, or supervised fine-tuning.\nStrong Python programming skills with PyTorch and Hugging Face Transformers.\nExperience building scalable backend services using FastAPI, Flask, or similar frameworks.\nFamiliarity with cloud platforms such as AWS, Azure, or GCP, including AI services like Bedrock, SageMaker, Vertex AI, or Azure OpenAI.\nExperience with Docker, Kubernetes, CI/CD, MLflow, LangSmith, observability, and model monitoring.\nStrong understanding of inference optimization, quantization, batching, caching, and GPU utilization.\n\nNice to Have: \nExperience with multimodal AI (Vision-Language Models, OCR, and Document AI).\nKnowledge of Knowledge Graphs or GraphRAG architectures.\nExperience with distributed training and large-scale model serving (vLLM, TensorRT-LLM, Triton).\nPublications in AI conferences (NeurIPS, ICML, ACL, EMNLP, CVPR, ICCV) or contributions to open-source AI projects.\nExperience building AI evaluation frameworks, safety guardrails, and hallucination mitigation systems.\n\nExpectation: \nYou enjoy solving complex AI problems and building production-grade systems.\nYou thrive in fast-paced environments and take ownership from architecture to deployment.\nYou enjoy mentoring engineers while remaining deeply hands-on.\nYou are passionate about Agentic AI, LLMs, and building the next generation of enterprise AI platforms.\nYou want to shape AI products used at scale and work on cutting-edge Generative AI technologies.\nSkills\nGraphRAG, Hugging Face, LLM, PyTorch, Python, RAG, Transformers, generative AI, large language model, large language models, retrieval augmented 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