AI Engineering Manager

Role & responsibilities

• Lead and manage a team of AI/ML Engineers, ML Engineers, and AI Developers to deliver production-grade AI solutions.
• Own the technical design, architecture, development, deployment, and maintenance of AI/ML systems.
• Translate business requirements into scalable AI/ML and Generative AI solutions.
• Drive development of LLM, RAG, NLP, computer vision, predictive analytics, and AI-agent applications.
• Establish engineering best practices for model development, evaluation, deployment, monitoring, and optimization.
• Build and maintain robust MLOps/LLMOps pipelines for continuous integration, deployment, and model lifecycle management.
• Review and optimize AI systems for performance, scalability, reliability, latency, and cost.
• Work closely with Data Science, Data Engineering, Product, Software Engineering, and DevOps teams.
• Guide the selection of models, frameworks, cloud platforms, databases, and AI infrastructure.
• Ensure AI applications meet requirements for security, privacy, data quality, responsible AI, and governance.
• Lead AI projects from proof of concept through production deployment and continuous improvement.
• Conduct technical reviews, establish coding standards, and mentor engineers.
• Define engineering roadmaps, project milestones, resource requirements, and delivery priorities.
• Monitor production systems and lead root-cause analysis and resolution of critical issues.
• Evaluate emerging AI technologies and identify opportunities to improve products and engineering processes.
• Communicate technical progress, risks, and business impact to senior leadership and stakeholders.

Preferred candidate profile

• 5 to 10 years of experience in AI/ML, Machine Learning Engineering, Software Engineering, or related fields, including demonstrated technical/team leadership experience.
• Strong hands-on expertise in Python, Machine Learning, Deep Learning, Generative AI, LLMs, and AI application development.
• Strong understanding of RAG, embeddings, vector databases, prompt engineering, fine-tuning, model evaluation, and AI agents.
• Experience with PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain/LangGraph, or equivalent frameworks.
• Strong knowledge of MLOps/LLMOps, including MLflow, model deployment, monitoring, CI/CD, and model lifecycle management.
• Experience deploying AI systems on AWS, Azure, or GCP.
• Strong proficiency in Docker, Kubernetes, Git, REST APIs, microservices, and distributed systems.
• Good understanding of SQL, data pipelines, data engineering, cloud infrastructure, and databases.
• Demonstrated experience taking AI/ML solutions from PoC to production at scale.
• Strong knowledge of software engineering principles, system design, architecture, testing, and performance optimization.
• Experience managing, mentoring, and developing AI/ML engineering teams.
• Strong technical leadership, problem-solving, project management, communication, and stakeholder-management skills.
• Ability to balance technical quality, delivery timelines, scalability, and business requirements.
• Bachelor’s degree in Computer Science, AI/ML, Data Science, Engineering, Mathematics, Statistics, or a related field; Master’s degree preferred.
• Experience with AI security, responsible AI, data privacy, and AI governance is an advantage.