{"id":3008547,"url":"https://alion.io/job/verbaflo-senior-ai-engineer-2","title":"Senior AI Engineer","company":{"id":2995,"name":"VerbaFlo","domain":"verbaflo.ai","url":"https://alion.io/company/verbaflo","size_band":"51-200","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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"Confluence","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Haystack","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SharePoint","optional":false},{"name":"SQL","optional":false},{"name":"Weaviate","optional":false},{"name":"AI Agents","optional":true},{"name":"AutoGen","optional":true},{"name":"CrewAI","optional":true},{"name":"GraphRAG","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-21T14:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-21T14:35:30Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"We are looking for a senior AI Engineer with strong hands-on experience in RAG, embeddings, and LLM-based applications. This is a hands-on, high-ownership role. You will be responsible for designing and building RAG pipelines, managing embedding and vector search infrastructure, integrating LLM capabilities into chatbot products, and working with different types of data to build reliable, production-grade AI systems.\n\nResponsibilities: \nDesign, build, and deploy end-to-end RAG pipelines from scratch.\nSelect, evaluate, and fine-tune embedding models and optimize retrieval quality.\nWork with vector databases such as Pinecone, Weaviate, Chroma, and pgvector.\nImplement advanced chunking strategies, including late chunking, to improve context fidelity and retrieval precision.\nBuild data ingestion pipelines from multiple sources, including SQL/NoSQL databases, PDFs, web content, Confluence, SharePoint, and real-time APIs.\nIntegrate RAG and LLM components into chatbot and conversational AI products.\nWork with LangChain, LlamaIndex, Haystack, or custom orchestration frameworks.\nBuild and maintain retrieval evaluation frameworks using RAGAS, triad evaluations, and other evaluation methods.\nImprove retrieval quality using metrics such as precision, recall, and relevance.\nDeploy and monitor LLM services on AWS, GCP, or Azure.\nImplement logging, monitoring, alerting, and MLOps practices for production AI systems.\nWork closely with product and engineering teams to build reliable, low-latency AI experiences.\n\nRequirements: \n5+ years of overall AI/ML engineering experience, including 3+ years of hands-on LLM engineering experience.\nProven experience designing and deploying RAG systems end-to-end.\nStrong understanding of embedding models and vector databases.\nStrong programming skills in Python.\nHands-on experience with LangChain, LlamaIndex, Haystack, or similar LLM orchestration frameworks.\nExperience working with structured, unstructured, and real-time data sources.\nPractical knowledge of late chunking and advanced retrieval strategies.\nExperience with cloud platforms such as AWS, GCP, or Azure.\nExperience with Docker and Kubernetes.\nStrong problem-solving skills with a focus on building production-ready systems.\n\nGood to Have: \nExperience with agentic AI frameworks such as AutoGen, CrewAI, or custom agent architectures.\nExposure to Graph RAG or Knowledge Graphs.\nOpen-source contributions in AI/ML.\nSkills\nLLM, LLMs, Python, RAG, large language model, large language models, retrieval augmented generation","description_format":"text","description_chars":2535,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents","Real Estate AI"],"lifecycle":[{"event":"open","at":"2026-10-21T14:35:30Z"}],"visa":[{"country":"GB","licensed_sponsor":true,"evidence":"Licensed UK visa sponsor (Skilled 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