GenAI Full-Stack Engineer Lead

We are seeking a results-driven Generative AI practitioner with end-to-end experience for the execution and deployment of cutting-edge Generative AI and agentic AI solutions across our enterprise-wide Controls Technology platform. In this role, you will be responsible for translating AI strategy into tangible, production-ready capabilities that enhance operational efficiencies and drive business value. We’re looking for someone who combines deep technical expertise in generative AI - including context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration - with a proven track record of successfully delivering complex technology projects. This role centers on architecting and delivering solutions built on pre-trained and hosted foundation models, not on training or fine-tuning models., * GenAI Delivery Leadership: Execute the delivery roadmap for generative and agentic AI projects, ensuring alignment with business objectives and timelines. Manage the project lifecycle from ideation and scoping to deployment and post-launch support.

• Team Leadership & Mentorship: Build, mentor, and manage a high-performing team of AI engineers and specialists. Foster a culture of execution, collaboration, and continuous improvement to successfully deliver on the AI roadmap.
• End-to-End Solution Delivery: Oversee the design, development, and deployment of robust, scalable, and production-ready GenAI and agentic applications. Ensure all solutions meet rigorous performance, security, and quality standards before and after deployment.
• Agentic Solution Delivery: Drive the design and delivery of agentic workflows and multi-agent systems, establishing standards for agent harnesses, orchestration patterns, and reliable long-running agent execution across the platform.
• Stakeholder & Program Management: Serve as the primary point of contact for GenAI delivery. Manage stakeholder expectations, communicate project progress, identify and mitigate risks, and ensure on-time and on-budget delivery.
• Cross-Functional Partnership: Collaborate closely with Data Mesh, Cloud Architecture, MLOps/LLMOps, and business unit teams to ensure the seamless integration and operationalization of GenAI and agentic solutions into our existing technology ecosystem.
• Technical Excellence & Best Practices: Drive the adoption of best practices in software development (CI/CD), LLMOps, agent observability, and project management (Agile/Scrum) within the AI team to ensure efficient and repeatable delivery.
• Governance & Ethical Deployment: Implement and enforce robust governance and ethical AI frameworks throughout the delivery process - including guardrails, agent isolation/sandboxing, and responsible AI practices - ensuring compliance with data privacy standards and corporate policies.

Technical Skills

• Programming Languages: Python (expert proficiency required)
• Python Packages: Transformers, LangChain, LlamaIndex, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, and other relevant data science, machine learning, and web development libraries.
• Deep Learning Frameworks: TensorFlow, PyTorch
• LLMs: Llama, Gemini, GPT, and other advanced LLMs.
• Vector Databases: Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions (pgvector).
• Cloud Platforms: AWS, Azure, GCP
• MLOps Tools: MLflow, Kubeflow, or similar.
• Containerization: Docker, Kubernetes
• CI/CD Tools: GitHub Actions, Jenkins, or similar.
• Version Control: Git
• Data Visualization & Reporting: Tableau, Power BI, matplotlib, seaborn.
• Databases: SQL and NoSQL databases

• 10+ years of overall experience.
• 8+ years of experience in AI/ML, with at least 3 years in Generative AI (including agentic AI).
• 5+ years of leadership experience managing technical teams and delivering complex software or AI solutions.
• Extensive hands-on experience with AWS services and infrastructure related to AI/GenAI.
• A strong portfolio of projects showcasing the successful delivery of AI solutions into a production business environment.

Education:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field (PhD preferred).

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