Overview

Position Title:Sr. AI Engineering Manager

Responsibilities

• Lead the end-to-end delivery of AI initiatives from ideation and prototyping through deployment, operationalization, and production scaling.
• Drive the transformation of manual and process-intensive workflows into intelligent, automated, AI-enabled solutions with measurable business impact.
• Design, engineer, and deploy reusable foundational AI capabilities, including containerized models, adaptation workflows, optimization frameworks, and reference architectures.
• Establish and enforce technical standards across machine learning, generative AI, multimodal systems, and production software engineering practices.
• Guide the transition of AI research and prototypes into secure, scalable, reproducible, and enterprise-ready solutions.
• Provide architectural oversight for AI applications, including context management, model integration, tool orchestration, Model Context Protocol (MCP) implementations, and secure data handling.
• Define and manage model evaluation frameworks, quality benchmarks, validation processes, data quality standards, and responsible AI guardrails.
• Partner with platform, infrastructure, and data engineering teams to optimize compute resources, data pipelines, deployment automation, experiment tracking, and observability platforms.
• Act as a trusted technical advisor to business stakeholders, IT teams, and leadership to identify, prioritize, and deliver high-value AI use cases.
• Lead, mentor, and develop engineers and applied scientists while fostering a culture of innovation, accountability, collaboration, and operational excellence.
• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Machine Learning, Applied Mathematics, Physics, or a related technical discipline.
• 13-18 years of experience within software engineering, enterprise technology, or IT organizations.
• 6-7+ years of hands-on experience in software engineering, systems architecture, or enterprise application development.
• 7+ years of dedicated AI/ML engineering experience, including significant experience deploying Generative AI solutions in production environments.
• Proven leadership experience managing and scaling engineering teams focused on AI, machine learning, foundation models, multimodal systems, or advanced analytics.
• Deep expertise with Kubernetes, container orchestration, distributed systems, and GPU infrastructure management.
• Hands-on experience with model serving and inference platforms such as Triton Inference Server, vLLM, TGI, or similar technologies.
• Experience building and orchestrating enterprise AI and agentic workflows using frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or related platforms.
• Strong understanding of MLOps practices, experiment tracking, model registries, CI/CD pipelines, deployment automation, and AI observability tools including MLflow, Weights & Biases, Prometheus, Grafana, Arize, or TruLens.
• Experience deploying AI solutions across one or more modalities including text, image, video, speech, audio, or multimodal systems.
• Strong knowledge of secure software development, enterprise architecture, middleware platforms, and infrastructure operations.
• Demonstrated ability to communicate complex technical concepts, trade-offs, and risks to both technical and executive audiences.
• Proven track record of balancing business value, engineering quality, cost optimization, and enterprise scalability.