Job Overview
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.
Key Responsibilities
- 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.
Required Technical Skills
- Core Generative AI Concepts: Deep understanding of foundation models, LLMs, embeddings, tokenization, and context-window management. Fluent in applying pre-trained and hosted models to enterprise use cases.
- Context Engineering: Expertise in advanced context engineering — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production.
- Prompt Engineering: Adept at advanced prompt engineering techniques and best practices, with familiarity with frameworks that facilitate effective prompt design and management.
- Retrieval-Augmented Generation (RAG): Advanced knowledge of RAG techniques, including hybrid search, multi-vector retrieval, Hypothetical Document Embeddings (HyDE), self-querying, query expansion, re-ranking, and relevance filtering.
- Knowledge Graphs & Graph RAG: Experience designing and delivering knowledge graphs (e.g., using graph databases such as Neo4j or ArangoDB) and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.
- Agentic AI & Multi-Agent Orchestration: Proven experience delivering agentic systems using Google Agent Development Kit (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.
- Agent Harness & Interoperability: Strong grasp of harness engineering (governance, constraints, feedback loops, execution controls, agent isolation/sandboxing) and agent interoperability protocols — the Model Context Protocol (MCP) for tool/data access and the Agent2Agent (A2A) protocol for inter-agent collaboration.
- Machine Learning Frameworks & Cloud Computing: Working knowledge of ML frameworks and extensive hands-on experience with AWS (or equivalent) services and infrastructure for AI/GenAI.
- Natural Language Processing (NLP) & AI Deployment: Advanced NLP skills (NER, dependency parsing, text classification, topic modeling). Expertise in containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for LLMOps.
- Data Engineering & API Development: Strong proficiency in data preprocessing, document ingestion, and handling large-scale datasets. Experience with real-time and streaming AI applications and designing RESTful APIs for model and agent integration.
- Generative AI Tools & Platforms: Experienced with LangGraph, Autogen, CrewAI, LangChain, LlamaIndex, Hugging Face, and Google ADK. Familiarity with major GenAI APIs (OpenAI, Gemini, Claude) and version control systems like Git.
- Agent Observability & Evaluation: Experience with tracing and evaluation tooling (e.g., OpenTelemetry-based observability) for production GenAI and agent systems.
- AI Compliance & Guardrails: Knowledge of AI compliance frameworks and best practices. Experience implementing guardrails to ensure ethical AI usage and mitigate risks (e.g., Microsoft's AI Guidance Framework).
Required Leadership & Soft Skills
- Delivery Leadership: Proven ability to lead and deliver complex, large-scale technical projects from concept to production.
- Program Management: Expertise in Agile/Scrum methodologies, project planning, resource allocation, and risk management.
- Strategic Execution: Capacity to translate high-level AI strategy into a concrete, actionable delivery plan and execute it effectively.
- Stakeholder Management: Exceptional ability to manage expectations, communicate complex technical topics clearly, and build strong relationships with both technical and non-technical stakeholders.
- Pragmatic Innovation: A passion for applying cutting-edge GenAI and agentic technologies to solve real-world business problems in a practical and efficient manner.
- Problem Solving: Proactive and analytical mindset to overcome technical and logistical challenges in a fast-paced environment.
Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field (PhD preferred).
- Total Experience of 14+ years. 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.
Job Family Group
Technology
Job Family
Applications Development
Time Type
Full time
Most Relevant Skills
Please see the requirements listed above.
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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