Manager, AI Engineering

You will be responsible for defining and governing AI project lifecycles from data acquisition and experimentation to production deployment monitoring and continuous optimization. You will lead AI engineers and data scientists establish best practices and drive enterprise-grade AI adoption using modern MLOps and LLMOps principles.

This role requires strong collaboration with business stakeholders product leaders engineering teams and enterprise architects to ensure AI investments are aligned with measurable business outcomes.

Strategic Thinking & Leadership

• Partner with business leaders to identify high-impact AI opportunities and translate them into scalable AI/ML solutions.
• Define and communicate AI product vision roadmaps and measurable success metrics.
• Drive AI strategy across predictive analytics Generative AI and intelligent automation initiatives.
• Establish governance frameworks for Responsible AI model explainability fairness and compliance.

• Lead cross-functional AI programs and influence executive stakeholders through compelling insights and presentations.

Technical Leadership & Expertise

• Architect and oversee end-to-end AI/ML and GenAI systems including:
  • Predictive analytics models
  • Deep Learning and Neural Networks
  • NLP and computer vision solutions
  • Retrieval-Augmented Generation (RAG) systems
  • Agentic AI frameworks and multi-agent orchestration systems
• Strong proficiency inGoogle Cloud Platform (GCP)services for AI/ML (Vertex AI BigQuery Dataflow Cloud Storage)
• Deep expertise in machine learning algorithms including ensemble methods neural networks regression models simulation and optimization techniques NLP and image processing
• Experience building AI systems usingTensorFlow PyTorch Keras and Python-based ecosystems
• Experience withLLMs foundation models prompt engineering fine-tuning and evaluation pipelines
• Implement scalableMLOps and LLMOpspractices including CI/CD for ML model versioning monitoring and automated retraining
• Proficiency inGit Docker API-based deployments and scalable cloud AI services
• Apply strong software engineering practices within AI systems including testing modular design observability and documentation
• Drive research and innovation in advanced AI techniques to enhance enterprise capabilities
• Support architectural reviews and ensure best practices across AI systems

• ImplementResponsible AIprinciples including governance model explainability fairness and ethical AI compliance

Delivery Focus

• Own end-to-end AI product delivery in partnership with Product Engineering and Data teams.
• Ensure production-grade deployment of AI models using containerization (Docker) orchestration and scalable cloud infrastructure.
• Influence investment decisions using measurable impact metrics and ROI analysis.

• Establish monitoring frameworks for model drift performance degradation and system reliability.

Team Development & Community Leadership

• Lead and mentor AI engineers and data scientists.
• Build AI engineering standards reusable frameworks and shared tooling across SSDA.
• Promote knowledge sharing through Communities of Practice.
• Foster a culture of experimentation continuous learning and engineering excellence.
• Support talent development in emerging AI domains including GenAI and agent-based systems.

Minimum Requirements

• Bachelors Degree in related fields (Data Science Machine Learning Computer Science Statistics Applied Mathematics IT or equivalent).
• 5 to 8 years of experience applying analytical methods and AI/ML solutions in enterprise environments.
• 5 to 8 years of experience using Python-based AI/ML technologies.
• Experience leading AI or Data Science teams.
• Experience of acting as a senior technical lead facilitating solution trade-offs and architectural decisions.
• Experience using Cloud AI Platforms (GCP preferred).

• Hands-on experience with Generative AI technologies and enterprise AI deployment.

Preferred Requirements

• Masters or PhD in Data Science Machine Learning Statistics Applied Mathematics or Computer Science.
• Experience managing and growing high-performing AI teams.
• Expert-level knowledge in advanced predictive analytics and AI techniques (Genetic Algorithms Ensemble Learning Neural Networks NLP Simulation Design of Experiments).
• Strong working knowledge of GCP and enterprise AI architecture patterns.
• Expertise in open-source technologies such as Python R Spark SQL.
• Experience building enterprise-grade GenAI and agent-based AI solutions.