Lead AI Engineer

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Overview

We are looking for a Head of Engineering to lead ourAIcapability spanningAI platforms machine learning systems and emerging agentic AI architectures. This role is responsible for drivingend-to-end solutioningfrom client discussions and problem framing to execution strategy and production delivery.

You will operate at the intersection ofclient advisory technical architecture and execution leadership building scalable foundations that power advanced AI and autonomous systems.

Key Responsibilities

1. Client Advisory & Problem Framing

• Lead senior-level client conversations to identify opportunities acrossdata AI and agentic workflows

• Translate ambiguous business problems into structuredAI/data solution blueprints

• Act as a trusted advisor onAI transformation roadmaps including platform and operating model design

• Present architectures trade-offs and execution strategies to technical and non-technical stakeholders

2. Data & AI Foundations

• Define and drive architecture formodern data platforms(data lakes lakehouses real-time pipelines)

• Build scalableML and AI foundationsincluding feature stores training pipelines and inference systems

• Establish reusable frameworks accelerators and reference architectures

• Ensure strong governance acrossdata quality lineage security and compliance

3. Agentic AI Systems

• Lead design and implementation ofagentic AI systems(LLM-based agents RAG pipelines multi-agent orchestration)

• Define patterns fortool use memory reasoning workflows and human-in-the-loop systems

• Evaluate and integrate emerging GenAI technologies into production-grade solutions

• Drive experimentation industrialization of agentic use cases

4. Execution Strategy & Delivery Leadership

• Own end-to-end delivery:discovery PoC MVP scaled production

• Build and lead cross-functional teams acrossdata engineering ML engineering and AI engineering

• Define delivery plans milestones and measurable outcomes

• Manage risks dependencies and client expectations in complex engagements

5. Engineering Excellence & MLOps

• Establish best practices acrossCI/CD MLOps LLMOps and observability

• Ensure robustness inmodel performance monitoring retraining and lifecycle management

• Drive standards forreproducibility evaluation and system reliability

6. Capability Building & Influence

• Build internal capability acrossAI foundations and agentic systems

• Mentor engineering leaders and shape technical culture

• Contribute tothought leadership GTM assets and reusable IP

• Influence senior stakeholders across client and internal ecosystems

Qualifications

• 15 to 20 years in engineering acrossdata platforms and AI/ML systems

• 5 years in leadership roles managing large distributed engineering teams

• Strong experience indata platforms (batch streaming) cloud ecosystems (AWS/Azure/GCP)

• Deep expertise inML systems MLOps and production AI architectures

• Hands-on experience withGenerative AI and LLM-based systems (RAG agents orchestration frameworks)

• Proven ability to leadclient-facing engagements and drive delivery from concept to production

• Excellent communication and executive stakeholder management skills

Preferred Qualifications

• Experience in consulting / AI services organizations

• Exposure to buildingagentic workflows in enterprise settings

• Familiarity with tools like LangChain LlamaIndex vector databases orchestration frameworks

• Experience across multiple industries and large-scale transformations

If you like wild growth and working with happy enthusiastic over-achievers youll enjoy your career with us!

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