Job Summary

This role is accountable for building, deploying, and maintaining robust machine learning pipelines and operational frameworks. The individual applies solid expertise in ML Ops and DevOps tools to automate workflows, optimize model lifecycle management, and ensure reliable delivery of ML solutions. They contribute to project success by implementing best practices, supporting process compliance, and providing technical input within the team.

Key Responsibilities

1. Implement and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment processes.
2. Apply DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and monitor pipeline health.
3. Utilize infrastructure-as-code tools such as Terraform and AWS CloudFormation to provision and manage scalable cloud resources for ML workloads.
4. Integrate monitoring solutions like Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production environments.
5. Ensure process compliance by using Git, GitHub, GitLab, and Bitbucket for version control and code management within the team.
6. Participate in technical discussions and feasibility studies to evaluate technical alternatives and support architecture best practices for ML Ops solutions.
7. Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities.

• Design and implement automation solutions for cross-system workflows in a large-scale enterprise environment
• Develop production-quality Python services, tools and utilities
• Integrate LLM-based capabilities into internal platforms and workflows
• Collaborate with engineers, product stakeholders and domain experts across multiple teams
• Write clean, maintainable, and well-documented code following best practices
• Build and maintain unit, integration, and end-to-end tests
• Participate in code reviews and contribute to engineering standards
• Leverage AI-assisted development tools to improve productivity and code quality
• Contribute to CI/CD pipelines, ensuring reliable build, test and deployment processes
• Apply Software Quality & Compliance (SQC) practices including testing, traceability and documentation

Skill Requirements

1. Solid Proficiency In Ml Ops, Including Automation Of Ml Pipelines And Model Lifecycle Management.
2. Solid Understanding Of Devops Tools Such As Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions For Workflow Automation.
3. Solid Experience With Python For Scripting, Data Processing, And Ml Pipeline Development.
4. Solid Knowledge Of Infrastructureascode Tools Like Terraform And Aws Cloudformation For Cloud Resource Management.
5. Solid Skills In Monitoring And Logging Tools Including Prometheus, Grafana, Elk Stack, And Fluentd.
6. Solid Familiarity With Version Control Systems Such As Git, Github, Gitlab, And Bitbucket.
7. Solid Ability To Participate In Technical Discussions And Support Process Compliance Within The Team.

Other Requirements

1. Optional but valuable:
2. AWS Certified DevOps Engineer
3. - Google Professional Machine Learning Enginee

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering or a related field
• 2–5 years of hands-on software development experience (or equivalent experience)
• Strong proficiency in Python
• Experience developing production-grade applications (code quality, testing, documentation)
• Familiarity with AI/ML concepts, especially LLMs and prompt-based workflows
• Experience with AI-assisted coding tools (e.g., GitHub Copilot, Claude, Windsurf or similar)
• Understanding of software architecture principles and working with distributed systems
• Experience working in a collaborative, multi-team environment

Preferred Qualifications

• Experience integrating APIs and working with enterprise systems
• Hands-on experience with CI/CD tools (e.g., Azure DevOps, GitHub Actions, Jenkins)
• Knowledge of containerization and deployment technologies (Docker, Kubernetes an advantage)
• Experience implementing automated testing frameworks and quality gates
• Familiarity with observability, logging and monitoring practices
• Exposure to data pipelines or workflow orchestration tools
• Understanding of secure coding practices and model/data protection (an advantage)

Soft Skills

• Strong problem-solving and analytical thinking
• Ability to work independently and take ownership of tasks
• Effective communication and collaboration skills
• Attention to detail and commitment to high-quality deliverables