{"id":1536787,"url":"https://alion.io/job/hcltech-associate-technical-consultant-2","title":"Associate Technical Consultant","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Azure","optional":false},{"name":"Copilot","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"RAG","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-09-30T19:16:48Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-08T21:35:06Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"Job Summary\nTo understand client requirements and propose solution pertaining to respective domain area.HCLTech is looking for a hands-on AI Builder to implement Generative AI solutions on the OpenAI platform under the direction of the Enterprise AI Architect. This role sits at the build layer of GenAI delivery: taking approved architecture and designs and turning them into working, tested, production-ready components - RAG pipelines, agents, copilots, and integrations - while following the security, Responsible AI, and operational standards set at the architecture level. You will work closely with the Enterprise AI Architect and delivery engineering teams to ship reliable GenAI features quickly and to a consistent quality bar\nKey Responsibilities\nBuild and implement Generative AI solution components (RAG pipelines, agentic workflows, copilots, chat/assistant interfaces) on the OpenAI API / Azure OpenAI Service, per architecture provided by the Enterprise AI Architect.\nDevelop and tune prompts, function/tool-calling definitions, and context/retrieval pipelines to meet accuracy, latency, and cost targets.\nIntegrate GenAI components with enterprise systems, data sources, and vector databases, following approved data-access and security patterns.\nImplement guardrails, content filters, and evaluation harnesses to support Responsible AI and quality requirements defined by the architecture team.\nWrite unit, integration, and evaluation tests for GenAI features; track accuracy, hallucination rate, and other quality metrics.\nSupport production readiness: contribute to LLMOps pipelines, logging/observability hooks, cost tracking, and deployment scripts under architect guidance.\nTroubleshoot and resolve issues in existing GenAI solutions, including prompt regressions, latency, and integration failures.\nDocument build decisions, prompt/version history, and configuration details to keep solutions maintainable and auditable.\nCollaborate with the Enterprise AI Architect on feasibility checks, effort estimates, and technical trade-offs during solution design.\nReuse and extend approved architecture patterns and reference implementations rather than building one-off solutions.\nSkill Requirements\nExperience delivering GenAI features in a client-facing systems integrator / consulting environment.\nExposure to multi-agent frameworks or orchestration tools beyond a single-agent RAG pattern.\nFamiliarity with LLMOps/MLOps tooling (e.g., MLflow, Azure ML, evaluation frameworks) for monitoring models in production.\nExperience with GitHub Copilot or similar AI-assisted engineering tools in the SDLC.\nBasic understanding of Responsible AI concepts (bias, fairness, hallucination mitigation) beyond implementation-level guardrails.\nOther Requirements\nnull","description_format":"text","description_chars":2772,"description_truncated":false,"requirements":{"experience_years_min":null,"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":["IT Consulting & Digital Transformation","IT Outsourcing & Dedicated Teams","Systems Integrators"],"lifecycle":[{"event":"open","at":"2026-09-30T19:16:48Z"}],"visa":[],"liveness":{"score":76,"band":"hot","label":"Hiring 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