{"id":2906170,"url":"https://alion.io/job/multicorewareinc-aiml-subject-matter-expert-data-analytics-genai","title":"AI/ML Subject Matter Expert – Data Analytics & GenAI","company":{"id":1764592,"name":"Multicorewareinc","domain":"multicorewareinc.com","url":"https://alion.io/company/multicorewareinc","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","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":71},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AutoGen","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Git","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"llama.cpp","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Ollama","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"vLLM","optional":false},{"name":"Weaviate","optional":false},{"name":"dbt","optional":true},{"name":"FastAPI","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-08-30T00:00:00Z","employer_posted_date":"2026-08-30","last_verified_at":"2026-10-25T19:55:25Z","board_verified":true,"closed_at":null,"days_open":56,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":56},"description":"We are looking\nfor a highly experienced AI/ML Subject Matter Expert with strong Data Analytics\nand GenAI implementation expertise. The role requires a hands-on individual\ncontributor who can design, build, and operationalize scalable analytics\nsolutions, KPI computation frameworks, data validation pipelines, machine\nlearning models, and governed GenAI capabilities over enterprise data.\nThe ideal\ncandidate will bring deep expertise in Python, SQL, analytics engineering,\nmachine learning, Retrieval-Augmented Generation, and Agentic AI frameworks,\nand should be comfortable working with complex business metrics, enterprise\ndata sources, and open-source LLM technologies to deliver reliable,\nexplainable, and production-ready analytical solutions.\nResponsibilities:\nAnalytics Engineering & KPI Development:\nDesign, develop, and maintain robust analytics\ncode using Python, pandas, and NumPy to compute, validate, reconcile, and\noperationalize key business metrics including costing, margins, QBR metrics,\nand operational performance indicators.\nBuild efficient data transformations, optimize\nperformance through vectorization and memory management, and implement\nrepeatable data pipelines with testing, logging, validation, and reconciliation\ncontrols.\nSQL & Data Extraction:\nDevelop advanced SQL queries to extract,\ntransform, and shape data from enterprise systems and cloud data warehouse\nplatforms.\nWork with complex joins, aggregations, window\nfunctions, query optimization, and governed metric definitions.\nGenerative AI / Ask-the-Data Prototype:\nImplement a governed GenAI prototype enabling\nusers to ask questions over structured and semi-structured enterprise data.\nUse Llama-family or comparable open-source models\nthrough Ollama, llama.cpp, vLLM, or similar inference frameworks.\nBuild Retrieval-Augmented Generation and Agentic\nAI pipelines across structured and semi-structured data.\nDesign chunking, embedding, retrieval, and\nreranking approaches.\nImplement agentic workflows using CrewAI,\nLangGraph, AutoGen, LlamaIndex Agents, or equivalent production-ready agent\norchestration frameworks.\nProduce structured responses such as tables,\nJSON, and drill-down-ready answers.\nImplement guardrails for grounded responses,\ncitations, traceability to source data, and safe handling of sensitive fields.\nSupport evaluation of GenAI and agentic outputs\nfor accuracy, groundedness, reliability, latency, and operational usability.\nMachine Learning & Advanced Analytics:\nApply light-to-moderate machine learning\ntechniques where appropriate, including anomaly detection, outlier\nidentification, cost variance analysis, feed failure detection, simple\nforecasting, trend analysis, model evaluation, and error analysis.\nExperimentation, Evaluation & Deployment:\nCreate reproducible experimentation workflows,\nincluding test question sets for LLM evaluation, accuracy and groundedness\nchecks, latency profiling, and performance tuning.\nPackage deliverables for deployment using Docker,\nconfiguration management, and produce clear technical documentation, runbooks,\nand handover materials.\nRequirements\nEducation:\nRelevant\ndegree in Computer Science, Data Science, Engineering, Mathematics, Statistics,\nor a related field.\nTechnical Skills (Must haves):\nExpert-level Python skills, particularly with\npandas and NumPy: data cleaning and transformation; joins, merges,\naggregations, and windowed calculations; time-series data handling; performance\noptimization, profiling, and memory management.\nStrong SQL expertise, including complex joins,\naggregates, window functions, and query tuning and optimization mindset.\nSolid understanding of statistics and machine\nlearning fundamentals, including feature engineering, model evaluation metrics,\noverfitting and validation concepts, and scikit-learn or equivalent ML\nlibraries.\nPractical GenAI implementation experience with\nLlama models or comparable open-source LLMs; Ollama or similar local inference\ntools; RAG and Agentic AI frameworks such as LangChain, LlamaIndex, LangGraph,\nCrewAI, AutoGen, or equivalent; and embeddings/vector stores such as FAISS,\npgvector, Weaviate, or Pinecone.\nStrong engineering discipline, including unit\ntesting and data testing, logging and error handling, Git-based development\nworkflows, CI basics, Docker, and environment management.\nNeed to have (Can be bridged):\nHands-on individual contributor experience\ndriving solution development from prototype through deployment-ready\ndeliverables.\nAbility to work with complex business metrics,\nenterprise data sources, and governed analytical solutions.\nAbility to collaborate with business and\ntechnology stakeholders and independently drive solution development.\nGood to have (Not essential):\nExperience with Snowflake or comparable modern\ncloud data platforms.\ndbt experience, including modeling, testing, and\ndocumentation.\nExperience with enterprise semantic layers or\ngoverned metric definitions.\nExperience building lightweight APIs using\nFastAPI or similar frameworks.\nFamiliarity with enterprise security concepts\nsuch as RBAC, data masking, sensitive data handling, and audit logging.\nPreferred Qualifications (Optional):\nExperience\nacross the typical technology stack: Python, pandas, NumPy, SQL, scikit-learn,\nJupyter, Git, Docker, FastAPI, LangChain, LlamaIndex, Ollama, Llama-family\nmodels, FAISS, pgvector, Weaviate, Pine cone, and Snowflake or equivalent cloud\ndata warehouse.","description_format":"text","description_chars":5421,"description_truncated":false,"requirements":{"experience_years_min":null,"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":[],"lifecycle":[{"event":"open","at":"2026-10-20T07:54:24Z"}],"visa":[],"liveness":{"score":20,"band":"cold","label":"Long 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