{"id":1273084,"url":"https://alion.io/job/thiran-traction-infotech-mlops-engineer","title":"MLOps Engineer","company":{"id":3809005,"name":"Thiran Traction Infotech","domain":"thirantraction.com","url":"https://alion.io/company/thiran-traction-infotech","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","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":22000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Argo Workflows","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"GitOps","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"ArgoCD","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Grafana","optional":true},{"name":"Helm","optional":true},{"name":"ONNX","optional":true},{"name":"Prometheus","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-08-29T05:02:59Z","employer_posted_date":null,"last_verified_at":"2026-08-29T05:02:59Z","board_verified":false,"closed_at":null,"days_open":49,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":49},"description":"Relevant Experience : 4 to 6 years\n\nJob Description :\n\nInfrastructure & Pipeline Development :\n\n- Design and maintain Argo Workflows / Argo Eventsbased ML and data processing pipelines across environments (dev / QA / staging / prod).\n\n- Design and maintain WorkflowTemplates, DAGs, fan-out patterns, scheduled workflows, and event-driven triggers following GitOps best practices.\n\n- Design and develop Python-based workflows and components for ML data processing and model inference.\n\n- Work with Databricks and Spark-based workloads for data processing, transformation, and pipeline execution.\n\n- Build and maintain Docker images for ML and GPU-accelerated workloads, including multi-stage builds and container optimization.\n\n- Develop scalable inference workflows using Ray where required.\n\n- Integrate with Azure ML and Azure cloud services for model versioning, model artifacts, training workflows, and deployment.\n\n- Maintain CI/CD pipelines with automated testing, quality gates, and environment-specific deployment processes.\n\nModel Deployment & Operations :\n\n- Automate deployment, monitoring, and rollback of machine learning and deep learning models on production Kubernetes environments.\n\n- Manage model versioning and artifact movement from cloud ML workspaces and storage systems to production workloads.\n\n- Maintain configuration systems and reusable workflow components to ensure reproducible pipeline execution across environments.\n\n- Monitor model and pipeline performance, data processing health, resource utilization, and infrastructure reliability.\n\n- Troubleshoot production issues involving failed workflows, data bottlenecks, resource contention, GPU utilization, and latency regressions.\n\n- Identify reliability and performance issues proactively and implement improvements to ML and data processing workflows.\n\n- Collaborate with data scientists and engineering teams to improve model deployment, inference, and pipeline reliability.\n\nExpertise and Qualifications :\n\nMust-Have Technical Skills :\n\n- Python : Strong programming and problem-solving skills; experience with application development, scripting, and Python packaging.\n\n- Workflow Design : Ability to design reliable workflows with dependencies, parallel execution, retries, failure handling, scheduling, and event-driven execution.\n\n- Argo Workflows / Argo Events : Understanding of WorkflowTemplates, DAGs, event-driven workflows, and workflow execution.\n\n- Databricks / Data Processing : Hands-on experience with Databricks, Spark/Spark SQL, or similar large-scale data processing platforms.\n\n- Kubernetes : Understanding of workload orchestration, resource management, namespaces, and production troubleshooting.\n\n- Git / Version Control : Strong understanding of Git or equivalent version control systems, including branching, merging, pull requests, and maintaining code/configuration changes.\n\n- Docker : Containerization, multi-stage builds, and optimization of ML workloads.\n\n- CI/CD : Experience designing pipelines with automated testing, quality gates, artifact management, and deployment processes.\n\n- Azure Cloud : Working knowledge of Azure ML, Azure Blob Storage, AKS, and Azure-based ML workflows.\n\n- Problem Solving : Ability to troubleshoot unfamiliar technical problems and identify practical solutions.\n\nGood-to-Have Technical Skills :\n\n- Ray : distributed data processing and inference at scale.\n\n- Deep Learning frameworks PyTorch, TensorFlow; 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