{"id":3571612,"url":"https://alion.io/job/tech-mahindra-ai-engineer-agentic-systems","title":"AI Engineer — Agentic Systems","company":{"id":5484,"name":"Tech Mahindra","domain":"techmahindra.com","url":"https://alion.io/company/tech-mahindra","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","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":["Pittsburgh, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":120000,"currency":"USD","period":"year","gross":null,"usd_annual":120000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Confluence","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Hugging Face","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Jira","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SharePoint","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Streamlit","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"Dagster","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"LoRA","optional":true},{"name":"MLFlow","optional":true},{"name":"PEFT","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prefect","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-10-31T17:35:38Z","employer_posted_date":"2026-10-31","last_verified_at":"2026-11-04T12:50:31Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Job Description\n\nSkill Set :\nSQL,Python,LLM,RAG,AI systems,Agent Design & Orchestration\n\nTotal Experience :\n5.00 to 10.00 Years\n\nNo of Openings :\n1\n\nJob Post Date :\n27/10/2026\n\nJob Expiry Date :\n30/11/2026\n\nDomain :\nIT\n\nLocation :\nPITTSBURGH [United States]\n\nJob Reference No :\n4114150\n\nJob Summary\nRole: AI Engineer - Agentic Systems\nLocation: Austin, TX\nExperience Level: 5+ Years Experience\nRole Summary:\nWe are hiring an AI Engineer to design, build, and operate agentic AI systems - LLM-powered agents that reason over enterprise data, call tools, and execute multi-step workflows end to end.\nThis is a hands-on engineering role at the center of how we automate complex technical and operational work. You will take ambiguous problems, decompose them into agent workflows, and ship production services that engineering teams depend on daily.\nThis is an ideal role for an engineer with a strong Python and ML foundation who has already shipped LLM or RAG applications and now wants to go deeper into orchestration, tool-calling, and autonomous multi-agent architectures. You will work within an established platform and architecture, with senior engineers available for design review - but you will own your components from prototype through production.\nKey Responsibilities:\nAgent Design & Orchestration\nBuild and ship LLM-powered agents that plan, call tools, and complete multi-step tasks using frameworks such as LangChain, LangGraph, or equivalent orchestration libraries.\nImplement multi-agent workflows - planner, executor, reviewer, and specialist roles - including state handling, hand-off logic, retries, and graceful failure paths.\nDesign and expose tools to agents via function-calling and Model Context Protocol (MCP) servers, wrapping internal APIs, databases, and enterprise systems as safe, well-scoped agent capabilities.\nRetrieval & Knowledge Systems:\nBuild and operationalize Retrieval-Augmented Generation (RAG) pipelines: chunking, embedding, vector and hybrid search, re-ranking, and context assembly.\nIntegrate structured, semi-structured, and unstructured sources - SharePoint, Confluence, Jira, REST APIs, Excel, and SQL warehouses - into retrieval and agent context layers.\nContinuously improve answer grounding and reduce hallucination through better retrieval, prompt design, and context management.\nEngineering & Deployment:\nDevelop production Python services and APIs for agent and model inference using FastAPI, containerized with Docker and deployed through standard CI/CD pipelines.\nInstrument agents with tracing, token and cost accounting, latency monitoring, and structured logging so that behavior is observable and debuggable in production.\nApply MLOps and LLMOps practices including version control for prompts and configs, automated regression testing, and safe rollout of model or prompt changes.\nEvaluation & Quality:\nBuild evaluation harnesses for agent workflows - golden datasets, task-level success metrics, LLM-as-judge scoring, and regression suites that gate releases.\nMeasure and tune for accuracy, latency, and cost, and make explicit trade-offs between them based on the use case.\nCollaboration:\nPartner with domain experts, data engineers, and product stakeholders to turn manual workflows into reliable automated agents.\nDeliver results through interfaces users actually adopt - Streamlit apps, chat interfaces, dashboards, and embedded integrations.\nWrite clear design notes and runbooks, and participate actively in code and design reviews.\nRequired Qualifications:\n5+ years of professional software, ML, or data engineering experience, including at least 1 year building LLM-based or GenAI applications.\nStrong Python engineering skills - not just notebooks. Production code, testing, packaging, async patterns, and API development with FastAPI or similar.\nHands-on experience with at least one LLM orchestration framework (LangChain, LangGraph, LlamaIndex, Semantic Kernel, or an equivalent) in a shipped application.\nDemonstrated experience building RAG systems: embeddings, vector stores (e.g., FAISS, pgvector, Pinecone, Chroma), and retrieval quality tuning.\nPractical command of prompt engineering, structured output, function/tool calling, and context-window management.\nWorking knowledge of SQL and cloud data platforms (Snowflake, Databricks, or equivalent), and comfort writing queries against enterprise schemas.\nExperience containerizing and deploying services with Docker and shipping through CI/CD on a major cloud (Azure, AWS, or GCP).\nSolid grounding in ML fundamentals and familiarity with PyTorch, TensorFlow, or HuggingFace Transformers.\nClear written and verbal communication, and the judgment to escalate when a problem is under-specified rather than guessing.\nPreferred Qualifications:\nExperience building or consuming Model Context Protocol (MCP) servers, or comparable agent tool-integration layers.\nProduction experience with multi-agent or agentic workflow systems, including human-in-the-loop checkpoints and approval gates.\nFamiliarity with LLM observability and evaluation tooling such as LangSmith, Langfuse, Weights & Biases, or MLflow.\nExperience with workflow orchestration tools - Airflow, Dagster, Prefect - or analytics engineering with DBT.\nExposure to model fine-tuning or parameter-efficient adaptation (LoRA/PEFT) and knowing when it is - and is not - the right answer.\nBackground in a technical engineering domain (semiconductor, hardware validation, manufacturing, or similar) where agents operate over specialized data.\nExperience with AI governance, responsible-AI review, or enterprise security requirements for LLM deployments.\nComments for Suppliers: Building AI Tools and flows for Server Validation\nMust Have:\nOptimizing Token utilization\nPython Scripting\nDatabase - Azure\nThe pay range for this role is $100,000- $120,000 per annum including any bonuses or variable pay. Tech Mahindra also offers benefits like medical, vision, dental, life, disability insurance and paid time off (including holidays, parental leave, and sick leave, as required by law). Ask our recruiters for more details on our Benefits package. The exact offer terms will depend on the skill level, educational qualifications, experience and location of the candidate.\nThis role is open for subcontracting: $65 - $75/ hr\n“Tech Mahindra is an Equal Employment Opportunity employer. We promote and support a diverse workforce at all levels of the company. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin, or disability. All applicants will be evaluated solely on the basis of their ability, competence, and performance of the essential functions of their positions with or without reasonable accommodations. Reasonable accommodations also are available in the hiring process for applicants with disabilities. Candidates can request a reasonable accommodation by contacting the company ADA Coordinator at .”","description_format":"text","description_chars":7006,"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":["Parental leave"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Information Technology","Professional Services","Systems Integrators","Business Process Outsourcing (BPO)"],"lifecycle":[{"event":"open","at":"2026-10-31T17:35:38Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 768 · green card filings: 5","filings_12m":768,"filings_prev_12m":1320,"green_card_filings_12m":5,"median_offered_wage_usd":112500,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":199}],"liveness":{"score":80,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.888,"p_room":0.9,"age_days":3,"expected_fill_days":9,"reasons":["conf:2","velocity","win:mid","comp:brand"],"computed_at":"2026-11-04T06:34:25Z"},"pay":{"stated_usd_annual":120000,"is_top_pay":false},"html_url":"https://alion.io/job/tech-mahindra-ai-engineer-agentic-systems","json_url":"https://alion.io/job/tech-mahindra-ai-engineer-agentic-systems.json","meta":{"generated_at":"2026-11-04T17:04:39Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":26147,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-05T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5484},"rest":"https://alion.io/mcp/rest/get_company?id=5484"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ftech-mahindra-ai-engineer-agentic-systems"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ftech-mahindra-ai-engineer-agentic-systems"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ftech-mahindra-ai-engineer-agentic-systems"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/tech-mahindra-ai-engineer-agentic-systems\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ftech-mahindra-ai-engineer-agentic-systems"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3571612}},{"tool":"search_jobs","arguments":{"company":"techmahindra.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3571612},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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