{"id":1253083,"url":"https://alion.io/job/glauben-technologies-mlops-engineer","title":"MLOps Engineer","company":{"id":3800360,"name":"Glauben Technologies","domain":"glaubentechnology.com","url":"https://alion.io/company/glauben-technologies","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":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India","Chennai, India","Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11500,"max_usd":30000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ArgoCD","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false}],"status":"live","first_seen_at":"2026-09-11T05:07:20Z","employer_posted_date":null,"last_verified_at":"2026-09-11T05:07:20Z","board_verified":false,"closed_at":null,"days_open":17,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":17},"description":"About the Role : \n\nWe are looking for an MLOps Engineer with hands-on experience in building, deploying, and managing machine learning workflows and production ML systems. The ideal candidate should have strong Python skills, experience with MLOps tools and practices, and a good understanding of cloud, containerization, CI/CD, and ML lifecycle management.\n\nResponsibilities : \n\n- Build and maintain ML workflows and MLOps pipelines across the machine learning lifecycle.\n\n- Develop and maintain automation for model training, validation, deployment, monitoring, and retraining.\n\n- Implement ML lifecycle management practices including experiment tracking, model versioning, and model registry.\n\n- Work with tools such as MLflow, Kubeflow, and Airflow for workflow orchestration and ML pipeline management.\n\n- Build and maintain CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or Argo CD.\n\n- Containerize ML applications and services using Docker and deploy them using Kubernetes.\n\n- Deploy and manage ML workloads on AWS, Microsoft Azure, or Google Cloud Platform.\n\n- Implement model monitoring, performance tracking, logging, and alerting for production ML systems.\n\n- Develop and integrate REST APIs for ML services using FastAPI or Flask.\n\n- Troubleshoot production ML infrastructure, deployments, and pipeline failures.\n\n- Work closely with Data Scientists, Data Engineers, Software Engineers, and DevOps teams to operationalize ML models.\n\n- Follow best practices for scalability, reliability, security, and reproducibility of ML systems.\n\nRequired Skills : \n\n- 2 - 7 years of experience in MLOps, ML Engineering, DevOps for ML, or a closely related role.\n\n- Strong hands-on programming experience in Python.\n\n- Strong understanding of Machine Learning workflows and the ML lifecycle.\n\n- Hands-on experience with MLOps pipelines and production model deployment.\n\n- Experience with MLflow, Kubeflow, or Airflow.\n\n- Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Argo CD.\n\n- Strong hands-on experience with Docker and Kubernetes.\n\n- Experience with at least one major cloud platform : AWS, Azure, or GCP.\n\n- Experience with model deployment, monitoring, versioning, experiment tracking, and model registry.\n\n- Experience developing REST APIs using FastAPI or Flask.\n\n- Good understanding of Linux and scripting.\n\n- Strong troubleshooting and problem-solving skills.\n\nSkills\nMLOps, Python, CI/CD Pipeline, Machine Learning, Docker, Kubernetes, AWS, ARGO Tool, Monitoring Tools, Performance Tuning","description_format":"text","description_chars":2538,"description_truncated":false,"requirements":{"experience_years_min":2,"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":[],"lifecycle":[{"event":"open","at":"2026-09-25T18:04:06Z"}],"liveness":{"score":30,"band":"fade","label":"Fading","p_open":0.85,"p_active":0.639,"p_room":0.55,"age_days":17,"expected_fill_days":15,"reasons":["seen:17","velocity","win:tail","comp:junior"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/glauben-technologies-mlops-engineer","json_url":"https://alion.io/job/glauben-technologies-mlops-engineer.json","meta":{"generated_at":"2026-09-28T23:50:25Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1327,"day_limit":5000,"remaining_today":3673,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}