ML Platform Engineer

Key Responsibilities

• Collaborate with data scientists to convert experimental ML and GenAI workflows into scalable, production-ready pipelines.
• Co-develop and maintain shared, version-controlled repositories with modular components.
• Design and manage CI/CD pipelines using Azure DevOps for deploying ML models and GenAI tools.
• Containerize applications using Docker and deploy them using cloud-native services.
• Automate deployment workflows and infrastructure setup using Terraform and schema management tools for Azure and Snowflake.
• Ensure smooth integration of model training, evaluation, and deployment stages across projects.
• Deploy and manage GenAI solutions including chatbots, RAG applications, and predictive analytics tools.
• Operationalize LLMs and GenAI agents, including prompt orchestration and chaining logic.
• Track model versions, prompt iterations, and performance metrics to support continuous improvement.
• Implement observability tools to monitor latency, token usage, hallucination rates, and user feedback.
• Ensure compliance with internal governance policies around data security and ethical AI.
• Set up alerting and rollback mechanisms to handle model or infrastructure failures.
• Participate in code reviews and knowledge sharing sessions.
• Participate in and plan/suggest improvement to the existing workflows that impact business and technical domains.

Key Qualifications & Requirements

Specific academic or professional qualifications/experience/competence necessary for this position

Education Qualification:

• Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.
• 2-3 years of experience in ML engineering, MLOps, platform engineering or DevOps role with exposure to Gen AI/ML workflows.

Technical Competence & Skills:

• Strong proficiency in Python, Bash, and scripting for automation & CI/CD tools.
• Hands-on experience with CI/CD tools (Azure DevOps preferred)
• Experience with Docker and cloud-native services (Azure & Snowflake preferred).

Leadership Competence:

• Strong problem solving & debugging skills
• Excellent communication & collaboration abilities
• Willingness to learn, experiment & contribute to evolving practices