{"id":2434899,"url":"https://alion.io/job/hcltech-senior-technical-specialist-54","title":"Senior Technical 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":"Sales","role_family":"Sales","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":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15000,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google GKE","optional":false},{"name":"IAM","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-13T10:22:43Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T22:22:56Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Job Summary\n\nSkill\n\nWhy It Matters\n\nVertex AI\n\nCore GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform.\n\nMLOps & ML Lifecycle Management\n\nUnderstanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems.\n\nPython & ML Frameworks\n\nStrong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development.\n\nData Engineering on GCP\n\nKnowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.\n\nVertex AI Pipelines / Kubeflow\n\nBuilding reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform.\n\nContainerization & Kubernetes\n\nDocker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice.\n\nModel Monitoring & Observability\n\nDetecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems.\n\nFeature Engineering & Feature Stores\n\nReusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability.\n\nCI/CD and Infrastructure as Code\n\nTerraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.\n\nCloud Architecture & Security\n\nUnderstanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.\n\nKey Responsibilities\n\nSkill\n\nWhy It Matters\n\nVertex AI\n\nCore GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform.\n\nMLOps & ML Lifecycle Management\n\nUnderstanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems.\n\nPython & ML Frameworks\n\nStrong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development.\n\nData Engineering on GCP\n\nKnowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.\n\nVertex AI Pipelines / Kubeflow\n\nBuilding reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform.\n\nContainerization & Kubernetes\n\nDocker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice.\n\nModel Monitoring & Observability\n\nDetecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems.\n\nFeature Engineering & Feature Stores\n\nReusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability.\n\nCI/CD and Infrastructure as Code\n\nTerraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.\n\nCloud Architecture & Security\n\nUnderstanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.\n\nSkill Requirements\n\nSkill\n\nWhy It Matters\n\nVertex AI\n\nCore GCP platform for model training, deployment, feature stores, pipelines, experiments, and monitoring. It is Google's primary ML platform.\n\nMLOps & ML Lifecycle Management\n\nUnderstanding model deployment, CI/CD, monitoring, retraining, governance, and automation is essential for production ML systems.\n\nPython & ML Frameworks\n\nStrong Python skills plus TensorFlow, PyTorch, Scikit-Learn, and related libraries remain the foundation for model development.\n\nData Engineering on GCP\n\nKnowledge of BigQuery, Dataflow, Pub/Sub, and Cloud Storage is critical because ML systems depend on reliable data pipelines.\n\nVertex AI Pipelines / Kubeflow\n\nBuilding reproducible and automated ML workflows is a key MLOps capability. Vertex AI Pipelines is Google's managed orchestration platform.\n\nContainerization & Kubernetes\n\nDocker and Google Kubernetes Engine (GKE) enable scalable training and inference workloads. Containerized ML workflows are a core MLOps practice.\n\nModel Monitoring & Observability\n\nDetecting model drift, performance degradation, data quality issues, and operational failures is vital for reliable ML systems.\n\nFeature Engineering & Feature Stores\n\nReusable, governed features improve model quality and consistency. Vertex AI Feature Store is a key GCP capability.\n\nCI/CD and Infrastructure as Code\n\nTerraform, Cloud Build, GitHub Actions, and deployment automation help deliver repeatable ML environments and releases.\n\nCloud Architecture & Security\n\nUnderstanding IAM, networking, service accounts, encryption, governance, and cost optimization is crucial for enterprise-grade AI solutions on GCP.\n\nOther Requirements","description_format":"text","description_chars":5023,"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":"2026-10-13T10:22:43Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":22,"reasons":["conf:0","win:early","comp:brand"],"computed_at":"2026-10-14T06:34:16Z"},"pay":null,"html_url":"https://alion.io/job/hcltech-senior-technical-specialist-54","json_url":"https://alion.io/job/hcltech-senior-technical-specialist-54.json","meta":{"generated_at":"2026-10-15T01:35:53Z","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":2182,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-16T00:00:00Z"}},"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-senior-technical-specialist-54"},{"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-senior-technical-specialist-54"},{"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-senior-technical-specialist-54"},{"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-senior-technical-specialist-54\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhcltech-senior-technical-specialist-54"}]}