Job Description

We are seeking an experienced AI Engineering Manager to lead a high-performing team of AI Engineers and Machine Learning specialists in designing, developing, and deploying cutting-edge Artificial Intelligence solutions. In this leadership role, you will drive the technical vision for AI initiatives, oversee end-to-end machine learning lifecycle management, and ensure the successful delivery of scalable AI products.

You will collaborate with Product Management, Data Science, Software Engineering, Cloud Infrastructure, and Executive Leadership to build innovative AI applications powered by Machine Learning, Deep Learning, Large Language Models (LLMs), and Generative AI technologies. The ideal candidate combines strong technical expertise with proven leadership skills to mentor engineering teams and deliver enterprise-grade AI solutions that create measurable business value.

Key Responsibilities

AI Strategy & Leadership

• Lead and mentor a team of AI Engineers, Machine Learning Engineers, and MLOps Engineers.
• Define and execute the organization’s AI engineering strategy and technical roadmap.
• Foster innovation and promote best practices in AI development.
• Collaborate with executive leadership to align AI initiatives with business objectives.
• Recruit, develop, and retain top AI engineering talent.

AI Solution Development

• Design and oversee the development of AI-powered applications and intelligent automation solutions.
• Guide the implementation of Machine Learning, Deep Learning, Computer Vision, NLP, and Generative AI models.
• Ensure high-quality, scalable, and secure AI software development.
• Review solution architecture and provide technical guidance throughout the development lifecycle.

Machine Learning & MLOps

• Oversee the complete machine learning lifecycle, from data preparation to model deployment and monitoring.
• Implement MLOps best practices using CI/CD pipelines for AI models.
• Monitor model performance, accuracy, and reliability in production.
• Optimize model scalability and operational efficiency.

Cloud & Infrastructure

• Manage AI infrastructure on AWS, Microsoft Azure, or Google Cloud Platform.
• Oversee GPU resource utilization and cloud cost optimization.
• Ensure AI platforms meet performance, availability, and security requirements.
• Support containerized deployments using Docker and Kubernetes.

Cross-Functional Collaboration

• Partner with Product Managers to define AI product requirements and delivery schedules.
• Collaborate with Data Scientists to transition research models into production.
• Work with Security, Compliance, and DevOps teams to ensure secure AI deployments.
• Communicate project progress, risks, and outcomes to executive stakeholders.

Governance & Compliance

• Ensure AI solutions comply with organizational policies and industry regulations.
• Promote Responsible AI, model transparency, fairness, and ethical AI practices.
• Implement governance processes for AI model documentation, validation, and monitoring.

Required Qualifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, or a related discipline preferred.
• 8–12+ years of experience in software engineering, machine learning, or AI development.
• 3–5+ years of experience leading engineering teams.
• Proven experience delivering enterprise-scale AI and machine learning solutions.
• Strong understanding of AI software architecture and cloud-native application development.

Technical Skills

• Python
• Java or C++
• TensorFlow
• PyTorch
• Scikit-learn
• Hugging Face Transformers
• LangChain
• OpenAI APIs
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Docker
• Kubernetes
• MLflow
• Apache Spark
• Databricks
• Git
• GitHub Actions
• Azure AI Services
• AWS SageMaker
• Google Vertex AI
• SQL
• NoSQL Databases

Preferred Certifications

• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• TensorFlow Developer Certificate
• Databricks Certified Machine Learning Professional
• Certified Kubernetes Administrator (CKA)

Core Competencies

• AI Engineering Leadership
• Machine Learning Architecture
• Generative AI
• Large Language Models (LLMs)
• MLOps
• Cloud Computing
• AI Product Development
• Technical Strategy
• Team Leadership & Mentoring
• Agile Project Management
• Problem Solving
• Stakeholder Communication
• AI Governance
• Innovation Management