Lead AI Engineer

We are looking for an experienced Lead AI Engineer to drive the design, development, and deployment of AI/ML-powered applications. Candidate should have strong hands-on experience in application development, lead and mentor a team of AI/ML developers, define best practices, and deliver scalable, production grade AI solutions aligned with business goals.

Key Responsibilities

• Design, develop, and deploy AI/ML applications, microservices, and APIs on Kubernetes-based infrastructure, ensuring scalability, reliability, and performance across development, staging, and production environments.

• Build and maintain end-to-end AI/ML pipelines covering deployment, monitoring, versioning, and continuous improvement using modern MLOps/AIOps tools and practices.

• Lead and mentor a team of AI/ML engineers, conduct code reviews, and define best practices.

• Continuously evaluate and adopt emerging AI tools, frameworks, LLM technologies, and open-source solutions to enhance platform capabilities and team productivity.

• Collaborate closely with Business Analysts, Architect and technical teams to align AI engineering efforts with business objectives and ensure secure, compliant solutions.

• Establish and maintain technical documentation, deployment runbooks and SOPs

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or a related field.

• 8–10 years of hands-on experience in software engineering, with a strong focus on AI/ML application development and deployment.

• Expertise in Kubernetes – container orchestration, Helm charts, pod management, scaling, and troubleshooting.

• Strong experience with MLOps/AIOps tools and practices (e.g., MLflow, Kubeflow, Airflow, model registries, monitoring frameworks).

• Hands-on experience with cloud platforms – Azure, AWS, or GCP, including their AI services.

• Strong programming skills in Python; familiarity with FastAPI, Flask, or similar frameworks is a plus.

• Hands-on experience with CI/CD pipelines and tools such as GitOps, Docker, Jenkins, or GitHub Actions.

• Lead and mentor development teams, drive delivery, and manage technical priorities.

• Experience working with GenAI frameworks (e.g., LangChain), and vector databases.

• Experience with observability and monitoring tools (Prometheus, Grafana, OpenTelemetry) for AI workloads.

• Good understanding of AI security, responsible AI principles, and governance frameworks.