About the Role

We are seeking a highly experienced AI Senior Architect & Tech Lead for an 18-month contract to drive the strategic technical vision, architectural design, and execution of our enterprise-grade Generative AI and Agentic solutions. In this role, you will bridge the gap between business strategy and deep technical execution, leading engineering teams to build, scale, and integrate advanced LLM and multi-agent systems with complex enterprise data structures. You will establish the architectural blueprints, governance frameworks, and production standards for scalable, safe, and reliable AI across the enterprise.

Key Responsibilities

Architectural Leadership & Strategy

Define the end-to-end technical architecture and roadmap for enterprise Generative AI applications, agentic workflows, and multi-agent systems. Lead technical design reviews, evaluate model trade-offs, and make definitive decisions on infrastructure, frameworks, and integration patterns. Author robust architectural blueprints, technical design documents, and governance standards to guide engineering teams.

Technical Leading & AI Engineering

Provide hands-on technical leadership and mentorship to a team of senior AI and data engineers, ensuring best practices in coding, CI/CD, and system design. Oversee the implementation of advanced Retrieval Augmented Generation (RAG), fine-tuning strategies, and automated AI orchestration patterns. Drive the development of reusable enterprise-wide APIs, foundational AI services, and shared component libraries.

Enterprise Platform & Data Integration

Design seamless integrations between AI applications and core enterprise data platforms (Databricks Lakehouse, Delta Lake, Unity Catalog). Architect scalable data pipelines, vector search strategies, and knowledge graphs required to power high-context AI agents. Maximize the value of cloud AI ecosystems, specifically Google Cloud Platform, Vertex AI, and Gemini Enterprise.

Governance, Risk & Ops

Define enterprise AI guardrails, observability frameworks, evaluations metrics (hallucination mitigation), and AI safety protocols (red-teaming). Partner with Cybersecurity, Data Governance, and Privacy teams to ensure compliance with Responsible AI practices. Establish MLOps/LLMOps production standards, monitoring infrastructure, and auto-scaling mechanisms for reliable operational delivery.

Requirements

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI, Data Science, or a related field (equivalent experience considered). 8+ years of total experience in software, data, or ML engineering, with at least 2-3 years specifically focused on designing and launching enterprise-scale AI/LLM architectures into production. Proven track record as a Tech Lead or Principal Architect delivering complex, cross-functional technology initiatives. Google Cloud (Professional ML Engineer/Cloud Architect) or Databricks certifications are highly desirable.

Technical Skills

GenAI Expertise: Mastery of LLMs, RAG, vector databases, prompt optimization, embeddings, and complex agentic frameworks (e.g., LangGraph, CrewAI, AutoGen). Cloud & Platform: Deep hands-on experience architecting on Google Cloud Platform (Vertex AI, Gemini Enterprise) and Databricks (PySpark, Delta Lake, Unity Catalog, MLflow). Software Engineering: Expert proficiency in Python, SQL, REST/gRPC APIs, Docker, Kubernetes, CI/CD pipelines, and modern automated testing. LLMOps & Security: Deep knowledge of AI evaluation frameworks, observability tools, token management, caching strategies, and data lineage.

Soft Skills & Leadership

Executive Communication: Ability to articulate complex AI concepts, architectural trade-offs, and risk profiles clearly to both C-level executives and technical developers. Influence & Collaboration: Proven capability to align and guide diverse teams across data engineering, cybersecurity, business units, and external vendors. Delivery & Ownership: A pragmatic, delivery-focused mindset with a dedication to engineering excellence, operational stability, and scalable design.