{"id":3207865,"url":"https://alion.io/job/sedin-technologies-senior-ai-engineer","title":"Senior AI Engineer","company":{"id":3024,"name":"Sedin Technologies","domain":"sedintechnologies.com","url":"https://alion.io/company/sedin-technologies","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":"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":{"min_usd":20000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"Mistral","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Falcon","optional":true},{"name":"Gradio","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Langfuse","optional":true},{"name":"LLMOps","optional":true},{"name":"MLFlow","optional":true},{"name":"Streamlit","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-10-24T14:35:15Z","employer_posted_date":null,"last_verified_at":"2026-10-24T14:35:15Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"We are looking for a passionate Senior AI Engineer to design, develop, and deploy Generative AI solutions using Large Language Models (LLMs), Retrieval Augmented Generation (RAG) pipelines, and modern AI frameworks like LangChain, LlamaIndex, and Hugging Face. You'll work on building intelligent, production-ready systems that enhance how users search, generate, and interact with information.\n\nResponsibilities: \nDesign and implement end-to-end Generative AI systems using LLMs and RAG architectures.\nBuild scalable retrieval and vector search pipelines using tools like Pinecone, FAISS, ChromaDB, or Milvus.\nDevelop custom agents, chains, and workflows using LangChain, LlamaIndex, or similar orchestration frameworks.\nIntegrate OpenAI, Anthropic, Llama, or Mistral models via APIs or fine-tuning workflows.\nOptimise prompt engineering, context retrieval, and embedding strategies for real-world use cases.\nCollaborate with backend teams to deploy AI microservices via FastAPI, Flask, or similar frameworks.\nImplement MLOps practices for monitoring, versioning, and continuous delivery of AI models.\nStay current with new developments in the LLM and GenAI ecosystem and recommend adoption strategies.\n\nRequirements: \nStrong programming skills in Python.\nHands-on experience with LangChain, LlamaIndex, or OpenAI / Hugging Face ecosystems.\nExperience building RAG pipelines and integrating vector databases (e. g., Pinecone, FAISS, ChromaDB).\nDeep understanding of LLM architectures, tokenisation, and prompt optimisation.\nExperience with PyTorch or TensorFlow for model training or fine-tuning.\nFamiliarity with Docker, Kubernetes, and cloud deployment (AWS / GCP / Azure).\nStrong grasp of data engineering workflows, API integration, and system design.\n\nGood to Have: \nExperience with LLMOps tools (e. g., Weights and Biases, MLflow, LangFuse, PromptLayer).\nExposure to open-source LLMs (Llama 3 Mistral, Falcon, etc. ) and custom fine-tuning.\nKnowledge of knowledge graph integration or context-aware retrieval.\nFamiliarity with frontend integration using Streamlit, Gradio, or similar frameworks.\nContributions to open-source AI projects, research papers, or public demos.\nSkills\nFAISS, LLM, Pinecone, Python, RAG, large 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