:

- About the job
- Build automate and scale intelligent systems that move seamlessly from experimentation to reliable production
- In this role you ll work at the intersection of DevOps and MLOps helping teams ship ML powered features faster safer and with measurable impact
- You ll partner closely with data scientists engineers and platform teams to create repeatable pipelines production grade deployments and solid observability across environments
- If you enjoy solving real world reliability challenges improving developer experience through automation and enabling ML models to perform consistently in production this is a great opportunity to grow your ownership and technical depth while contributing to a collaborative high learning culture

Key Responsibilities:

- Key Responsibilities
- Platform Automation
- Design and maintain CI CD workflows to automate build test release and deployment processes for ML and supporting services
- Implement infrastructure automation and configuration management to ensure consistent environments across dev staging and production
- Improve system reliability through monitoring alerting incident response practices and post incident improvements
- MLOps Model Delivery
- Build and manage ML pipelines for training validation packaging and deployment with reproducibility and traceability
- Enable model versioning artifact management and controlled rollouts e
- g
- canary blue green for ML services
- Establish model performance monitoring drift detection signals and feedback loops for continuous improvement
- Collaboration Engineering Excellence
- Work with data science teams to productionize Python ML code with robust testing packaging and runtime optimization




- Define operational standards logging metrics SLOs and contribute to documentation and runbooks
- Participate in code reviews and propose improvements to security scalability and cost efficiency
- Minimum Qualifications
- BTECH MTECH MCA MSC or equivalent practical experience
- 2 3 years of hands on experience in DevOps and or MLOps focused engineering roles
- Working experience with CI CD concepts and automation for deployments and releases
- Practical experience supporting Python based ML workloads packaging environments dependency management runtime troubleshooting
- Strong understanding of Linux fundamentals networking basics and system troubleshooting

Technical Requirements:

- SKILLS
- DevOps MLOps PythonML
- Good to have skills
- Docker Kubernetes Terraform MLflow Airflow

Additional Responsibilities:

- Preferred Qualifications
- Experience productionizing ML workflows end to end training pipelines model registry artifacts deployment monitoring
- Exposure to containerization and orchestration for scalable ML services e
- g
- Docker Kubernetes
- Familiarity with Infrastructure as Code and configuration tools e
- g
- Terraform Ansible
- Experience with ML lifecycle tooling e
- g
- MLflow Kubeflow and workflow orchestration e
- g
- Airflow
- Hands on exposure to LLM enabled applications including deployment patterns inference optimization and evaluation monitoring approaches
- Strong communication skills to align platform practices across engineering and data science stakeholders

Preferred Skills:

Technology->Devops->Ansible,Technology->AI-AI Engineering->MLOps,Technology->OpenSystem->Python - OpenSystem,Technology->AI-Data science->Machine Learning

📌 DevOps+MLOps+PythonML (Bengaluru)
🏢 Infosys
📍 Bengaluru