Senior AI Engineering Manager

About the role

We are seeking a high‑performing AI Engineering & Software Manager to lead the execution, deployment, and operationalization of AI solutions across the enterprise. The role combines deep software engineering expertise, advanced AI/ML knowledge, and strong delivery leadership to drive development, deployment, and scaling of AI‑powered products and platforms.

Key responsibilities

• Lead end‑to‑end delivery of AI initiatives from ideation through production scaling.
• Transform manual, process‑intensive workflows into intelligent, automated AI‑enabled solutions.
• Design, engineer, and deploy reusable foundational AI capabilities such as containerized models and reference architectures.
• Establish and enforce technical standards for machine learning, generative AI, multimodal systems, and production software engineering.
• Provide architectural oversight for AI applications, including model integration, tool orchestration, and secure data handling.
• Define and manage model evaluation frameworks, quality benchmarks, validation processes, and responsible AI guardrails.
• Partner with platform, infrastructure, and data engineering teams to optimize compute resources, data pipelines, deployment automation, and observability.
• Mentor and develop engineers and applied scientists while fostering a culture of innovation and operational excellence.

Required profile

• Bachelor’s, Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, Applied Mathematics, Physics or related field.
• 13‑18 years of experience in software engineering, enterprise technology or IT, with 7+ years dedicated to AI/ML engineering.
• Proven leadership experience managing and scaling engineering teams focused on AI, foundation models or advanced analytics.
• Strong understanding of secure software development, enterprise architecture and infrastructure operations.
• Demonstrated ability to communicate complex technical concepts to both technical and executive audiences.

Required skills

• Kubernetes and container orchestration.
• GPU infrastructure management.
• Model serving platforms such as Triton Inference Server, vLLM, TGI.
• Frameworks for enterprise AI workflows: LangChain, LangGraph, LlamaIndex, AutoGen.
• MLOps tools: MLflow, Weights & Biases, Prometheus, Grafana, Arize, TruLens.
• Experience with multimodal AI systems (text, image, video, speech, audio).
• Secure data handling and enterprise‑grade software development practices.