Job Description:

About the job
Join a fast moving team where you ll help shape reliable scalable and secure platforms that power modern machine learning solutions
In this role you ll blend strong DevOps practices with MLOps execution and Python based ML workflows to ensure models move smoothly from experimentation to production without compromising performance observability or governance
You ll collaborate closely with data scientists engineers and stakeholders to automate pipelines standardize deployments and improve the end to end lifecycle of ML systems
If you enjoy solving real world delivery challenges building repeatable automation and enabling teams to ship ML features confidently this opportunity offers hands on ownership continuous learning and a culture that values collaboration and practical innovation

Key Responsibilities:

Key Responsibilities
DevOps Platform Enablement
Design implement and maintain CI CD pipelines for applications and ML services across environments
Automate infrastructure provisioning and configuration to improve reliability repeatability and deployment speed
Establish monitoring logging and alerting practices to improve system observability and incident response
Ensure secure access controls secrets management and environment hygiene across development and production
MLOps ML Delivery
Build and maintain ML pipelines for training validation packaging and deployment of models using Python based workflows
Enable model versioning reproducibility and controlled rollouts e
g
canary blue green for ML services
Partner with data science teams to productionize models and define operational SLAs for ML endpoints and batch jobs
Implement automated quality checks for data model artifacts to reduce regressions and improve release confidence
LLM Enablement
Support deployment patterns for LLM based services including scalable inference prompt version management and runtime monitoring
Collaborate on integrating LLM capabilities into existing platforms with a focus on reliability latency and cost awareness

Technical Requirements:

Primary skills DevOps MLOps PythonML Domain Turbomachinery Compressor Rotor Technology Data Science Machine Learning Technology DevOps Continuous delivery Continuous deployment and release Technology Machine Learning Python

Additional Responsibilities:

Minimum Qualifications
Bachelor s degree or equivalent in Engineering Technology Computer Science BTech BE or equivalent Master s MTech MCA MSc is acceptable as listed
3 5 years of experience in DevOps and MLOps focused delivery for production systems
Hands on experience with Python based ML workflows and operationalizing ML models into services or batch pipelines
Strong understanding of CI CD concepts release management and environment promotion strategies
Experience implementing monitoring and operational practices for reliability and troubleshooting in production
Preferred Qualifications
Experience building and operating end to end MLOps pipelines including model packaging deployment automation and lifecycle governance
Practical exposure to LLM solution delivery including inference deployment prompt iteration workflows and evaluation monitoring approaches
Familiarity with containerization and orchestration for ML workloads and optimizing deployments for performance and scalability
Experience with infrastructure automation and configuration management to support repeatable ML environments
Proven ability to collaborate across data science and engineering teams translating experimentation needs into production grade systems
Good to have skills
Kubernetes Docker Terraform MLflow Apache Airflow

Preferred Skills:

Technology->DevOps->Continuous delivery - Continuous deployment and release,Technology->AI-AI Engineering->MLOps,Technology->AI-Data science->PYTHON,Technology->AI-Data science->Machine Learning

DevOps+MLOps+PythonML Developer