Machine Learning Engineer

AI / ML Engineer

Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Machine Learning Operations
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met.
Roles & Responsibilities:
- Expected to be an SME.
- Collaborate and manage the team to perform.
- Responsible for team decisions.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for their immediate team and across multiple teams.
- Mentor junior professionals to enhance their skills and knowledge.
- Continuously evaluate and improve existing processes and workflows.
Professional & Technical Skills:
- Strong Engineering experience with advance python skills.
- ML Pipeline Development: Design, build, and maintain scalable pipelines for model training to support our AI initiatives.
- Model Deployment & Serving: Deploy machine learning models as robust, secure services – containerize models with Docker and serve them via FastAPI ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.
- CI/CD Automation: Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.
- Model Lifecycle Management: Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory
- Monitoring & Optimization: Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.
- Must To Have Skills: Proficiency in Machine Learning Operations.
- Good exposure of cloud based services including AI services.
- Must have thorough understanding of infrastructure need for MLOps implementation.
- Must have python skills.
- Should have Multi Cloud skills
- Experience with Machine learning frameworks
- Ability to implement and optimize machine learning models for production environments.

Additional Information:
- The candidate should have minimum 5 years of experience in Machine Learning Operations.
- This position is based at our Bengaluru office.
- A 15 years full time education is required.