Engineering Manager - Machine Learning & AI

We are seeking an experienced Engineering Manager to lead our Machine Learning and AI engineering team. This role combines technical leadership with people management, requiring someone who can drive AI/ML product development while building and mentoring high-performing engineering teams.

Key Responsibilities

Technical Leadership

• Lead the design and implementation of scalable ML/AI systems and infrastructure
• Define technical roadmaps and architecture decisions for ML products and platforms
• Drive development of Generative AI applications and LLM-powered products
• Oversee development of LLM agents, multi-agent systems, and autonomous AI workflows
• Lead fine-tuning, prompt engineering, and optimization of large language models
• Ensure best practices in ML model development, deployment, monitoring, and governance
• Drive adoption of MLOps practices and tools across the organization
• Collaborate with data scientists to productionize research models and algorithms
• Oversee model performance monitoring, A/B testing, and continuous improvement processes

Strategic Planning

• Partner with product management to define AI/ML product strategy and priorities
• Translate business requirements into technical specifications and project plans
• Manage project timelines, resource allocation, and delivery commitments
• Communicate technical progress and challenges to senior leadership
• Identify opportunities to leverage AI/ML for competitive advantage

Operational Excellence

• Establish engineering processes and standards for ML model lifecycle management
• Ensure compliance with data privacy, security, and regulatory requirements
• Drive initiatives to improve development velocity and system reliability
• Champion responsible AI practices and ethical considerations in model development

Required Qualifications

Experience

• 9-12 years of software engineering experience with 3-5 years in management roles
• 6+ years of hands-on experience with machine learning and AI technologies
• 2+ years of experience with Generative AI, LLMs, and foundation models
• 2+ years managing technical teams of 5+ engineers
• Proven track record of delivering ML/AI products at scale

Technical Skills

• Strong programming proficiency in Python, with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Deep understanding of ML algorithms, model training, evaluation, and deployment
• Hands-on experience with Large Language Models (LLMs) and foundation models (GPT, BERT, LLaMA, etc.)
• Proficiency in prompt engineering, fine-tuning techniques (LoRA, QLoRA), and LLM optimization
• Experience building LLM agents, tool-calling systems, and multi-agent architectures
• Experience with Generative AI frameworks and tools (Langchain, LlamaIndex, Hugging Face)
• Knowledge of vector databases, embeddings, and retrieval-augmented generation (RAG) systems
• Experience with cloud platforms (AWS/GCP/Azure) and ML infrastructure tools
• Knowledge of data engineering, ETL pipelines, and data warehousing
• Familiarity with containerization (Docker, Kubernetes) and CI/CD practices
• Understanding of distributed systems and microservices architecture

Leadership Skills

• Excellent communication and interpersonal skills
• Experience coaching and developing technical talent
• Strong project management and organizational abilities
• Ability to make decisions in ambiguous situations
• Experience building inclusive and collaborative team cultures

Preferred Qualifications

• Advanced degree (MS/PhD) in Computer Science, Engineering, Mathematics, or related field
• Experience building and deploying production LLM applications and chatbots
• Hands-on experience with autonomous AI agents and task automation systems
• Knowledge of agent planning algorithms, tool integration, and multi-step reasoning
• Experience with function calling, code generation, and API integration for LLM agents
• Knowledge of transformer architectures, attention mechanisms, and modern NLP techniques
• Experience with multi-modal AI systems combining text, vision, and audio
• Familiarity with AI safety, alignment, and responsible AI development practices
• Experience with specific AI domains (NLP, computer vision, recommendation systems, etc.)
• Knowledge of ML governance, model risk management, and regulatory compliance
• Experience with real-time ML systems and edge deployment
• Background in A/B testing, experimentation platforms, and statistical analysis