About the Role :

We are looking for a seasoned Senior AI Lead to lead the engineering team responsible for building Incedo's Agentic AI and Generative AI platforms and products. In this role, you will own the AI technology roadmap, define reference architectures for agentic systems across the portfolio and establish engineering standards for building scalable, secure and production-grade AI solutions.

This is a hands-on technical leadership position requiring strong expertise in software engineering, LLM architecture, agentic systems and enterprise AI platforms. You will remain actively involved in architecture decisions, code reviews, model evaluations and production engineering while leading and mentoring a high-performing AI engineering team.

What You'll Own :

1. AI Architecture & Technology Strategy :

- Own and drive the technology roadmap for Agentic AI and Generative AI solutions.

- Define enterprise-wide reference architectures and reusable design patterns for agentic systems.

- Architect production-grade RAG, LLM and multi-agent applications with tool calling, memory and MCP-based enterprise integrations.

- Drive architectural decisions around foundation models, model selection, fine-tuning, orchestration and deployment strategies.

- Evaluate emerging AI technologies, frameworks and platforms to identify opportunities for innovation.

- Establish scalable and reusable AI engineering components across multiple products and platforms.

2. AI Evaluation, Quality & Governance :

- Define and own the enterprise AI evaluation strategy, including offline evaluations, regression suites and human-in-the-loop validation.

- Establish LLM quality benchmarks covering accuracy, relevance, hallucinations, safety and performance.

- Drive production LLM observability, monitoring and continuous improvement.

- Define governance frameworks for responsible AI, PII protection, prompt security and model risk management.

- Establish standards for prompt versioning, reproducibility, testing, code quality and AI engineering practices.

- Optimize model performance, infrastructure costs, token consumption and application latency.

3. AI Platform Engineering & Delivery :

- Lead the development and deployment of enterprise-grade AI platforms and products.

- Drive solutions from architecture and prototyping through production deployment and scale.

- Define cloud-native architecture and deployment standards across Azure, AWS and GCP environments.

- Ensure integration of AI capabilities with enterprise applications, APIs, data platforms and business workflows.

- Partner with Product, Solution Architecture and Engineering leaders to translate business strategy into executable technical roadmaps.

- Drive engineering excellence through CI/CD, Kubernetes, infrastructure automation and LLMOps practices.

4. Technical & People Leadership :

- Lead, mentor and develop a team of AI engineers and technical leads.

- Set technical direction, conduct architecture reviews and drive engineering standards.

- Stay hands-on with critical design decisions, code reviews, proof of concepts and model evaluations.

- Drive technical hiring, capability development and AI engineering talent strategy.

- Build a culture of innovation, ownership, collaboration and continuous learning.

- Influence cross-functional stakeholders and drive alignment on enterprise AI architecture and technology decisions.

What You'll Bring :

- 9 - 13 years of experience in software engineering, AI/ML engineering or technology development.

- At least 4 - 6 years of hands-on experience building and deploying AI/ML and GenAI solutions.

- Strong software engineering fundamentals and programming expertise in Python.

- Proven experience architecting and delivering production-grade LLM and GenAI applications.

- Deep understanding of agentic AI architectures, multi-agent orchestration, tool calling, memory and MCP integrations.

- Strong hands-on expertise in frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen or LlamaIndex.

- Expertise in RAG architecture, retrieval optimization, embeddings and vector databases.

- Experience with foundation models and platforms such as Azure AI/OpenAI, AWS Bedrock or Google Vertex AI.

- Strong understanding of LLM evaluation frameworks, observability, guardrails and LLMOps/MLOps.

- Experience designing and delivering cloud-native applications using Docker, Kubernetes and CI/CD.

- Proven ability to define technical architecture, engineering standards and reusable platform capabilities.

- Experience leading engineering teams, mentoring technical talent and driving cross-functional initiatives.

- Strong stakeholder management, technical communication and decision-making skills.