{"id":2802496,"url":"https://alion.io/job/bharathire-ai-technical-lead-machine-learning","title":"AI Technical Lead - Machine Learning","company":{"id":3806741,"name":"BharatHire","domain":"bharathire.com","url":"https://alion.io/company/bharathire-com","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":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":61000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":44},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Milvus","optional":true},{"name":"OpenAI","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"SQL","optional":true},{"name":"Vertex AI","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-08-11T11:57:47Z","employer_posted_date":null,"last_verified_at":"2026-08-11T11:57:47Z","board_verified":false,"closed_at":null,"days_open":70,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":70},"description":"Job Description :\n\nWe are looking for an AI Technical Lead with 6 to 11 years of experience to design and deliver enterprise-grade AI solutions using Generative AI, Multi-Agent Systems, RAG, MCP, and LLM fine-tuning. The ideal candidate should combine strong hands-on expertise in AI engineering and architecture with the ability to lead technical teams and drive end-to-end enterprise AI initiatives.\n\nKey Responsibilities :\n\n- Design and define scalable AI architectures for enterprise use cases involving LLMs, RAG, Multi-Agent Systems, and AI-powered applications.\n\n- Architect and develop Multi-Agent Systems with effective agent orchestration, tool integration, memory, and workflow management.\n\n- Design and implement RAG pipelines, including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and context management.\n\n- Build and integrate solutions using Model Context Protocol (MCP) and other mechanisms for connecting AI models with enterprise tools, systems, and data sources.\n\n- Design and implement LLM fine-tuning and model adaptation strategies based on business requirements and domain-specific use cases.\n\n- Apply context engineering techniques to improve model accuracy, relevance, reliability, and response quality.\n\n- Develop scalable AI applications and supporting data pipelines using Python and modern AI/ML frameworks.\n\n- Design knowledge graphs and structured knowledge representations to support intelligent retrieval and reasoning.\n\n- Integrate LLMs with enterprise applications, APIs, databases, and external tools.\n\n- Establish practices for prompt engineering, evaluation, observability, guardrails, security, and responsible AI.\n\n- Drive AI solution deployment across cloud and enterprise environments with focus on scalability, performance, reliability, and cost optimization.\n\n- Lead technical discussions, architecture reviews, and design decisions across AI engineering teams.\n\n- Mentor engineers and provide technical direction on AI architecture, development, and deployment practices.\n\n- Collaborate with product, data, engineering, security, and business stakeholders to translate business requirements into scalable AI solutions.\n\n- Evaluate emerging AI technologies and frameworks and identify opportunities to improve enterprise AI capabilities.\n\nRequired Skills & Experience:\n\n- 6-11 years of experience in AI/ML, software engineering, or related technical domains, with significant hands-on experience in Generative AI.\n\n- Strong hands-on experience with Multi-Agent Systems, RAG, MCP, and LLM-based application development.\n\n- Experience with LLM fine-tuning, model adaptation, prompt engineering, and context engineering.\n\n- Strong proficiency in Python and experience with AI/ML development frameworks.\n\n- Strong understanding of LLM architecture, embeddings, vector databases, retrieval techniques, and AI application patterns.\n\n- Experience designing enterprise AI architectures and taking solutions from proof of concept to production.\n\n- Experience building scalable data and knowledge pipelines for AI applications.\n\n- Good understanding of knowledge graphs, semantic search, and structured/unstructured data processing.\n\n- Experience with LLM evaluation, monitoring, guardrails, and AI application performance optimization.\n\n- Strong understanding of APIs, microservices, cloud platforms, and scalable deployment architectures.\n\n- Proven ability to lead technical teams and drive architecture and engineering decisions.\n\n- Strong communication, stakeholder management, and problem-solving skills.\n\nPreferred Skills :\n\n- Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar.\n\n- Experience with MCP servers, tools, and enterprise integrations.\n\n- Hands-on experience with vector databases such as Pinecone, Milvus, Weaviate, pgvector, or equivalent.\n\n- Experience with cloud AI platforms such as AWS Bedrock, Azure OpenAI, or Google Vertex AI.\n\n- Exposure to MLOps, CI/CD, Docker, Kubernetes, and production AI observability.\n\n- Experience delivering AI solutions in large enterprise 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