{"id":2072147,"url":"https://alion.io/job/hcltech-technical-support-specialist-6","title":"Technical Support Specialist","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":"Support","role_family":"Support","seniority":"middle","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":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AIOps","optional":false},{"name":"Datadog","optional":false},{"name":"Dynatrace","optional":false},{"name":"GCP","optional":false},{"name":"Incident Management","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"BigQuery","optional":true},{"name":"Databricks","optional":true},{"name":"Gemini","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Google Bigtable","optional":true},{"name":"ITIL","optional":true},{"name":"LLM","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Redis","optional":true},{"name":"SQL","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-10-08T07:33:10Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T10:27:33Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Job Summary\nGenAI & ML Operations Engineer\nRole Summary\nThe GenAI & ML Operations Engineer is responsible for ensuring the reliability, availability, observability, and operational support of Production Generative AI and Machine Learning solutions. The role focuses on monitoring AI/ML platforms, resolving incidents, implementing observability, supporting model upgrades, executing minor enhancements, and driving operational excellence across AI products and services.\nThe engineer works closely with Data Scientists, ML Engineers, Platform Engineers, Product Teams, and Business Stakeholders to maintain stable, scalable, and well-governed AI solutions in production.\nKey Responsibilities\nAI/ML Production Operations\nMonitor and support production GenAI and ML workloads, Perform daily operational health checks and service validations.\nTroubleshoot and resolve operational issues impacting AI services, Support L1/L2/L3 incident management activities.\nEnsure SLA, availability, and performance objectives are met.\nObservability & Monitoring\nDesign, implement, and maintain AI/ML observability solutions, Create dashboards, reports, and operational scorecards.\nConfigure alerts for failures, threshold breaches, latency, data quality, model performance, and infrastructure issues.\nMonitor model health, drift, performance, usage, and cost metrics, continuously improve monitoring coverage and alert effectiveness.\nIncident & Problem Management\nRespond to production incidents and service disruption, Conduct root cause analysis (RCA) and document findings.\nDrive preventive actions to reduce recurring incidents, Participate in major incident support and service restoration activities.\nModel Lifecycle Support\nSupport AI/ML model upgrades, version management, Monitor production behaviour following model changes and perform post-deployment validation.\nSupport retraining, tuning, and model refresh initiatives.\nContinuous Improvement & Enhancements\nDeliver minor enhancements to AI applications and operational tooling, Automate repetitive operational tasks where feasible.\nIdentify opportunities to improve reliability, efficiency, and supportability, Contribute to AI Ops, MLOps, and operational maturity initiatives.\nDocumentation & Governance\nMaintain runbooks, SOPs, knowledge articles, and support documentation, Document incidents, problems, technical debt, risks, and remediation plans.\nSupport change management and release governance processes, Ensure operational compliance with security and Responsible AI requirements.\nRequired Skills & Experience\nTechnical Skills\nGenerative AI, LLMs, RAG, and AI application support.\nMachine Learning lifecycle and MLOps concepts.\nMonitoring and Observability Platforms (Datadog, Dynatrace.).\nCloud Platforms (GCP), Kubernetes and containerized workloads, MemoryStore, Tracing, Storage, GAR.\nPython and automation scripting.\nOperational Skills\nIncident Management, Problem Management, Change Management\nRoot Cause Analysis\nService Reliability Engineering (SRE)\nOperational Reporting and Governance, Stakeholder Communication\nPreferred Experience\n8+ years in IT Operations, DevOps, SRE, Cloud Operations, MLOps, or AI Operations.\n3+ years of experience with GenAI / LLM platforms / ML Models\nExperience in working with Databricks jobs & pipelines, Catalog, compute, models, SQL warehouse.\nGCP workloads, GCP Deployments, Vertex AI, Gemini Enterprise Agent Development Kit, BigQuery, BigTable, Redis, Postgres\nExperience supporting production AI/ML solutions, working knowledge of ITIL-based operational processes\nFamiliarity with GenAI platforms, LLM-based applications, and AI observability practices.\nKey Responsibilities\nGenAI & ML Operations Engineer\nRole Summary\nThe GenAI & ML Operations Engineer is responsible for ensuring the reliability, availability, observability, and operational support of Production Generative AI and Machine Learning solutions. The role focuses on monitoring AI/ML platforms, resolving incidents, implementing observability, supporting model upgrades, executing minor enhancements, and driving operational excellence across AI products and services.\nThe engineer works closely with Data Scientists, ML Engineers, Platform Engineers, Product Teams, and Business Stakeholders to maintain stable, scalable, and well-governed AI solutions in production.\nKey Responsibilities\nAI/ML Production Operations\nMonitor and support production GenAI and ML workloads, Perform daily operational health checks and service validations.\nTroubleshoot and resolve operational issues impacting AI services, Support L1/L2/L3 incident management activities.\nEnsure SLA, availability, and performance objectives are met.\nObservability & Monitoring\nDesign, implement, and maintain AI/ML observability solutions, Create dashboards, reports, and operational scorecards.\nConfigure alerts for failures, threshold breaches, latency, data quality, model performance, and infrastructure issues.\nMonitor model health, drift, performance, usage, and cost metrics, continuously improve monitoring coverage and alert effectiveness.\nIncident & Problem Management\nRespond to production incidents and service disruption, Conduct root cause analysis (RCA) and document findings.\nDrive preventive actions to reduce recurring incidents, Participate in major incident support and service restoration activities.\nModel Lifecycle Support\nSupport AI/ML model upgrades, version management, Monitor production behaviour following model changes and perform post-deployment validation.\nSupport retraining, tuning, and model refresh initiatives.\nContinuous Improvement & Enhancements\nDeliver minor enhancements to AI applications and operational tooling, Automate repetitive operational tasks where feasible.\nIdentify opportunities to improve reliability, efficiency, and supportability, Contribute to AI Ops, MLOps, and operational maturity initiatives.\nDocumentation & Governance\nMaintain runbooks, SOPs, knowledge articles, and support documentation, Document incidents, problems, technical debt, risks, and remediation plans.\nSupport change management and release governance processes, Ensure operational compliance with security and Responsible AI requirements.\nRequired Skills & Experience\nTechnical Skills\nGenerative AI, LLMs, RAG, and AI application support.\nMachine Learning lifecycle and MLOps concepts.\nMonitoring and Observability Platforms (Datadog, Dynatrace.).\nCloud Platforms (GCP), Kubernetes and containerized workloads, MemoryStore, Tracing, Storage, GAR.\nPython and automation scripting.\nOperational Skills\nIncident Management, Problem Management, Change Management\nRoot Cause Analysis\nService Reliability Engineering (SRE)\nOperational Reporting and Governance, Stakeholder Communication\nPreferred Experience\n8+ years in IT Operations, DevOps, SRE, Cloud Operations, MLOps, or AI Operations.\n3+ years of experience with GenAI / LLM platforms / ML Models\nExperience in working with Databricks jobs & pipelines, Catalog, compute, models, SQL warehouse.\nGCP workloads, GCP Deployments, Vertex AI, Gemini Enterprise Agent Development Kit, BigQuery, BigTable, Redis, Postgres\nExperience supporting production AI/ML solutions, working knowledge of ITIL-based operational processes\nFamiliarity with GenAI platforms, LLM-based applications, and AI observability practices.\nSkill Requirements\nGenAI & ML Operations Engineer\nRole Summary\nThe GenAI & ML Operations Engineer is responsible for ensuring the reliability, availability, observability, and operational support of Production Generative AI and Machine Learning solutions. The role focuses on monitoring AI/ML platforms, resolving incidents, implementing observability, supporting model upgrades, executing minor enhancements, and driving operational excellence across AI products and services.\nThe engineer works closely with Data Scientists, ML Engineers, Platform Engineers, Product Teams, and Business Stakeholders to maintain stable, scalable, and well-governed AI solutions in production.\nKey Responsibilities\nAI/ML Production Operations\nMonitor and support production GenAI and ML workloads, Perform daily operational health checks and service validations.\nTroubleshoot and resolve operational issues impacting AI services, Support L1/L2/L3 incident management activities.\nEnsure SLA, availability, and performance objectives are met.\nObservability & Monitoring\nDesign, implement, and maintain AI/ML observability solutions, Create dashboards, reports, and operational scorecards.\nConfigure alerts for failures, threshold breaches, latency, data quality, model performance, and infrastructure issues.\nMonitor model health, drift, performance, usage, and cost metrics, continuously improve monitoring coverage and alert effectiveness.\nIncident & Problem Management\nRespond to production incidents and service disruption, Conduct root cause analysis (RCA) and document findings.\nDrive preventive actions to reduce recurring incidents, Participate in major incident support and service restoration activities.\nModel Lifecycle Support\nSupport AI/ML model upgrades, version management, Monitor production behaviour following model changes and perform post-deployment validation.\nSupport retraining, tuning, and model refresh initiatives.\nContinuous Improvement & Enhancements\nDeliver minor enhancements to AI applications and operational tooling, Automate repetitive operational tasks where feasible.\nIdentify opportunities to improve reliability, efficiency, and supportability, Contribute to AI Ops, MLOps, and operational maturity initiatives.\nDocumentation & Governance\nMaintain runbooks, SOPs, knowledge articles, and support documentation, Document incidents, problems, technical debt, risks, and remediation plans.\nSupport change management and release governance processes, Ensure operational compliance with security and Responsible AI requirements.\nRequired Skills & Experience\nTechnical Skills\nGenerative AI, LLMs, RAG, and AI application support.\nMachine Learning lifecycle and MLOps concepts.\nMonitoring and Observability Platforms (Datadog, Dynatrace.).\nCloud Platforms (GCP), Kubernetes and containerized workloads, MemoryStore, Tracing, Storage, GAR.\nPython and automation scripting.\nOperational Skills\nIncident Management, Problem Management, Change Management\nRoot Cause Analysis\nService Reliability Engineering (SRE)\nOperational Reporting and Governance, Stakeholder Communication\nPreferred Experience\n8+ years in IT Operations, DevOps, SRE, Cloud Operations, MLOps, or AI Operations.\n3+ years of experience with GenAI / LLM platforms / ML Models\nExperience in working with Databricks jobs & pipelines, Catalog, compute, models, SQL warehouse.\nGCP workloads, GCP Deployments, Vertex AI, Gemini Enterprise Agent Development Kit, BigQuery, BigTable, Redis, Postgres\nExperience supporting production AI/ML solutions, working knowledge of ITIL-based operational processes\nFamiliarity with GenAI platforms, LLM-based applications, and AI observability practices.\nOther Requirements\nNone","description_format":"text","description_chars":11004,"description_truncated":false,"requirements":{"experience_years_min":3,"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":"2026-10-08T07:33:10Z"}],"visa":[],"liveness":{"score":99,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.993,"p_room":1,"age_days":3,"expected_fill_days":17,"reasons":["conf:5","wave","velocity","win:early","comp:brand"],"computed_at":"2026-10-12T06:55:09Z"},"pay":null,"html_url":"https://alion.io/job/hcltech-technical-support-specialist-6","json_url":"https://alion.io/job/hcltech-technical-support-specialist-6.json","meta":{"generated_at":"2026-10-12T11:04:52Z","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":17181,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-13T00:00:00Z"}},"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":225},"rest":"https://alion.io/mcp/rest/get_company?id=225"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fhcltech-technical-support-specialist-6"},{"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%2Fhcltech-technical-support-specialist-6"},{"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%2Fhcltech-technical-support-specialist-6"},{"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/hcltech-technical-support-specialist-6\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhcltech-technical-support-specialist-6"}]}