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.