Lead AI Engineer

** Technical Leadership & Engineering*
* Design Agentic Systems:
Design and scale robust, secure, and production-ready multi-agent workflows, orchestrations, and advanced RAG architectures, in collaboration with Enterprise Architecture principles. Drive delivery by designing and building agentic solutions, spanning from piloting to full implementation.
Define Engineering Excellence:
Establish strict coding standards, code review processes, testing frameworks, and evaluation metrics for generative AI applications. Support and strictly enforce the standards set by the Head of Engineering and Technical Architect.
Cloud & Platform Integration:
Partner closely with our GCP and Data Engineering teams to build seamless LLMOps/MLOps CI/CD pipelines, ensuring scalable and cost-effective model deployment via Vertex AI and containerized environments. Take ownership of building and maintaining robust LLMOps pipelines.
AI Safety & Guardrails:
Implement robust evaluation frameworks, latency monitoring, and automated guardrails to ensure enterprise-grade safety, security, and compliance.
Internal AI Enablement & Prompt Lifecycle:
Manage the engineering workflows, CI/CD pipelines, version control, and evaluation frameworks for internal developer-facing AI assets, including prompt libraries and automated testing agents.

** Team Mentorship & Delivery*
* Grow the team:
Act as a technical mentor to a team of intermediate and junior AI Engineers, fostering a culture of continuous learning, clean code, and agility.
Pragmatic Delivery:
Collaborate with the AI Product Owner and Business Analysts to translate abstract business use cases into structured, achievable technical sprints.
Drive MVP to Production:
Shift the team’s focus from sandboxed proof-of-concepts (PoCs) to reliable, resilient applications deployed to production for global users, leading AI engineering for AI team solutions and actively supporting junior engineers through this transition.

** Evolve AI Maturity*
* Support the company’s evolving AI strategy, providing an expert voice on Use Case identification, platform identification and tool selection.
Advise the AI portfolio Lead in scaling impact and AI capability across the company, beyond the Group AI Team.
Stay up to date on market trends, new opportunities, and the changing landscape of AI technologies.

• ** Experience**
• ** AI Orchestration & Development:
  ** Expert-level experience building complex LLM-powered systems and multi-agent workflows using frameworks like Lang Graph, Lang Chain, Auto Gen, or ADK.
• ** Artificial Intelligence & Machine Learning:
  ** Deep practical understanding of machine learning algorithms, natural language processing (NLP) techniques, and the optimization of large language models for enterprise deployment.
• ** Data Engineering & Cloud Computing:
  ** Strong proficiency in designing optimized ELT/ETL pipelines and managing data lake/data warehouse architectures. Hands‑on experience with Google Cloud Platform (GCP) services including Vertex AI, Big Query, Cloud SQL, and Google Cloud Storage (GCS).
• ** MLOps/LLMOps Pipelines:
  ** Proven track record of architecting pipelines for model deployment, performance tracking, hyperparameter tuning, and containerized workflows using Docker and Kubernetes (GKE/Vertex AI Pipelines).
• ** Team Leadership & Mentorship:
  ** Extensive experience leading technical delivery, defining engineering milestones, running code reviews, and successfully mentoring junior or intermediate engineering talent.
• ** Knowledge & Skills**
• ** Autonomy:
  ** Works with high autonomy under broad strategic guidance. Accountable for defining and meeting technical, architectural, and delivery goals, establishing milestones, and assigning tasks.
• ** Influence:
  ** Exerts major technical influence across the organization, partners, and peers. Partners with cross‑functional business and product leaders, making key architectural decisions that affect budgets, timelines, and scalability.
• ** Complexity:
  ** Manages diverse, highly complex, and unpredictable technical challenges. Uses fundamental engineering principles to connect cutting‑edge AI research with practical enterprise software.
• ** Business
  
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