{"id":3387614,"url":"https://alion.io/job/hcltech-senior-technical-architect-83","title":"Senior Technical Architect","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":"Backend","role_family":"Backend","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":34000,"max_usd":84000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-10-28T08:26:33Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-10-29T04:46:30Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Summary\nLLMOps Engineer\nRole Summary\nThe LLMOps Engineer is responsible for operationalizing, scaling, monitoring, and governing Large Language Model (LLM)-based systems in production. This role focuses on ensuring LLM applications are reliable, performant, cost-efficient, observable, and secure across their lifecycle-from deployment to continuous optimization\nKey Responsibilities\nKey Responsibilities\nDeploy and manage LLM-based applications across cloud and hybrid environments.\nBuild and maintain production-grade inference pipelines for LLM workloads.\nDesign and implement LLMOps pipelines for model, prompt, and configuration lifecycle management.\nImplement monitoring for latency, throughput, cost, errors, drift, and hallucinations.\nBuild dashboards and alerts to ensure SLA and reliability targets.\nOptimize inference cost and performance using caching, batching, routing, and autoscaling strategies.\nEnforce security, access control, audit logging, and governance controls for LLM usage.\nCollaborate with Responsible AI teams to operationalize safety and compliance guardrails.\nEnable AI Engineers and Fullstack teams through reusable LLMOps frameworks and platforms.\nSkill Requirements\nRequired Skills & Experience\nStrong understanding of LLMs, Generative AI architectures, and inference workflows.\nHands-on experience running LLM-based systems in production environments.\nStrong experience with cloud platforms (Azure, AWS, or GCP).\nHands-on experience with Docker, Kubernetes, and container orchestration.\nExperience with CI/CD for ML or AI applications.\nProgramming and automation skills in Python or similar languages.\nFamiliarity with MLOps / LLMOps tooling, monitoring, and logging systems.\nExperience Level\n6-10 years of overall experience in DevOps, Platform, or ML/AI Engineering roles.\n2-4+ years of experience operating ML or GenAI systems in production.\nSuccess Measures\nStable, scalable, and cost-efficient LLM systems running in production.\nReduced production incidents and faster recovery for AI services.\nFaster and safer enterprise adoption of GenAI solutions enabled through robust LLMOps practices.\nOther 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