AI Engineering Lead

Role: You ll need to work closely with Data Scientists, Data Engineers, UI Engineers, Software Developers, and DevOps teams to design and implement end-to-end AI/GenAI solutions.

You will:

Be responsible for training, integrating, and deploying AI/GenAI/ML models in Azure/AWS ecosystem.

Design and implement enterprise-quality AI/GenAI/ML applications.

Optimize deployed models for performance, scalability, and efficiency.

Debug and troubleshoot issues in AI/GenAI/ML pipelines and systems.

Monitor model performance in production and retrain as necessary to maintain accuracy.

Stay conversant with the latest trends, techniques, and tools in GenAI pipelines. Experiment with state-of-the-art methods and propose innovative solutions to complex problems.

Excellent written and verbal communication skills required.

Technical Skills:

Excellent Python, PySpark, and SQL skills.

Knowledge of GenAI stack is a big plus.

Experience with Azure DevOps / AWS DevOps for ML projects, Databricks, CI/CD pipelines, and repositories.

Experience with Azure/AWS MLOps/Kubeflow.

Experience of training and deploying AI/GenAI/ML models into production environments and integrating them with existing systems.

Knowledge of containerization tools like Docker, orchestration systems like Kubernetes, and inference platforms like Kserve / NVIDIA Triton.

Knowledge of Azure / AWS ETL pipelines is a plus (but not mandatory).

Soft Skills:

Excellent written and verbal communication.

Exceptional problem-solving and multitasking skills in a fast-paced environment.

Experience Level: 8+ years of experience in ML Engineering.