The incumbent shall be responsible for leading AI/ML operational and platform engineering functions across the organization. The focus of this role is to provide and ensure technical excellence in the AI/ML technology landscape and drive the adoption of AI solutions across business units.

Relevant Experience:

• 10 years’ experience in IT, software engineering, or data science related positions
• 5 years of direct experience with AI/ML technologies, platforms, and solutions in production environments.
• 3 years in technical leadership, architecture, or senior engineering role.
• Experience in Designing and implementing large-scale AI/ML solutions and systems.
• Experience working with cloud platforms and understanding of cloud-native architectures.
• Experience with DevOps, CI/CD pipelines, and containerized deployments.

Responsibilities:

• Deep dive technical investigation, analysis and troubleshooting across AI/ML technology stacks, frameworks, and infrastructure using appropriate diagnostic and monitoring tools.
• Provide technical leadership and mentoring to AI Engineering, Data Engineering, and Platform teams, fostering a culture of technical excellence and continuous improvement.
• Drive the enablement of AI/ML platforms, tooling, and best practices across the organization.
• Provide architecture guidance and technical oversight for AI/ML solution design, implementation, and optimization.
• Lead the evaluation, selection, and integration of AI/ML tools, frameworks, and cloud services (e.g., Azure AI, AWS SageMaker, Google Vertex AI).
• Establish and maintain monitoring, logging, and observability standards for AI/ML systems and models.
• Investigate opportunities for optimization of AI/ML technology stacks, including model performance tuning and infrastructure efficiency.
• Work with solution architects and business stakeholders to translate business requirements into technical AI/ML solutions.
• Provide support and enablement for containerized and cloud-native environments, specifically Kubernetes and serverless platforms.
• Ensure compliance, security, and governance best practices are implemented across all AI/ML solutions.
• Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Mandatory Skills:

• Machine Learning Frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)
• AI/ML Platforms (Azure AI, AWS SageMaker, Google Vertex AI, Databricks)
• Large Language Models and GenAI (transformers, RAG, prompt engineering, LLMOps)
• Data Processing and Analytics (Spark, Hadoop, pandas, SQL)
• Cloud Platforms (Azure, AWS, GCP)
• Container Orchestration (Kubernetes, Docker)
• MLOps and Model Deployment tools (MLflow, Kubeflow, DVC, Weights & Biases)
• Data Engineering and ETL tools
• Monitoring, Logging, and Observability tools (Prometheus, ELK, Grafana, DataDog)
• Scripting and Programming Languages (Python, Java, Scala, SQL)
• Git and Version Control Systems

Must-have skills

AI/ML technology, AI/ML operational

Good-to-have skills

Engineering Manager