Senior AI Delivery Lead LLM Solutions Architect

Job Description

Role: Senior AI Delivery Lead / LLM Solutions Architect

Experience: 15–25 years

Location: Remote

Employment Type: Full-time

Overview

We are seeking a highly seasoned Senior AI Delivery Lead with deep expertise in Large Language Model (LLM) solution design and enterprise AI delivery governance. This role requires a dual focus: serving as the primary Delivery Lead for multiple concurrent AI programs (2× large engagements) while also acting as the senior architect responsible for prompting frameworks, retrieval strategies, and model selection to ensure scalable, high-quality solution outcomes.

The ideal candidate brings a unique combination of AI/LLM technical depth, multi-team governance experience, and strong delivery leadership capabilities across complex, cross-functional environments.

Key Responsibilities

Delivery Leadership and Governance

• Drive all delivery ceremonies including sprint planning, backlog refinement, stand-ups, retrospectives, and release readiness.
• Own risk, dependency, and issue tracking across multiple teams and technology streams.
• Maintain delivery health dashboards and proactively implement corrective actions.
• Coordinate inter-team and cross-vendor activities to ensure alignment of workstreams, milestones, and quality outcomes.
• Manage communications with customer stakeholders, executives, and internal leadership across two simultaneous AI program deliveries.
• Ensure compliance with delivery governance frameworks, SLAs, and change-control processes.
  
  

• Define and standardize prompt patterns, prompting frameworks, safety layers, and guardrails.
• Design retrieval architectures including chunking strategies, vectorization schemas, embedding model selection, retrieval pipelines, and relevance optimization.
• Evaluate and recommend LLM model choices (OSS, proprietary, fine-tuned models) based on use case, performance, cost, security, and latency constraints.
• Guide teams on RAG system architecture, observability, evaluation pipelines, and hallucination-reduction strategies.
• Drive architectural reviews and provide governance for model lifecycle management, experimentation, A/B testing, and performance optimization.
  
  

• Lead solution design sessions, technical deep dives, and architectural decision-making across AI/LLM components.
• Provide mentorship and technical leadership to cross-functional teams (data engineers, ML engineers, prompt engineers, QA, DevOps, product owners).
• Serve as a thought partner to clients on AI roadmaps, scaling strategies, and enterprise-grade deployment patterns.
• Ensure end-to-end solution reliability, including retrieval pipelines, orchestration, monitoring, and fallback strategies.
  
  

• 15–25 years of experience in AI/ML, with at least 5–8 years in LLM, transformer-based architectures, and enterprise AI implementations.
• Proven track record leading large AI programs as a Delivery Lead, Program Manager, or Engagement Lead while doubling as a senior AI architect.
• Hands-on experience with:
• Prompt engineering and prompt architecture patterns
• RAG design, embedding strategies, retrieval optimization
• Model selection (LLMs, OSS models, fine-tuned models)
• AI evaluation frameworks, metrics, and observability pipelines
• Strong understanding of enterprise delivery management (Agile/Scrum, scaled agile, governance cadences).
• Ability to manage and govern 2× parallel projects of significant scale.
• Excellent communication, stakeholder management, and cross-team coordination skills.
  
  

• Experience with vector databases, feature stores, or knowledge graphs.
• Familiarity with MLOps tooling, LLMOps, evaluation pipelines, and productionizing AI workloads.
• Background in cloud platforms (Azure, AWS, GCP) and associated AI services.
• Exposure to regulatory compliance, ethics, and AI safety principles.
• Demonstrated ability to mentor teams and drive best practices across engineering and delivery functions.
  
  

• High-quality, scalable AI/LLM architectures across enterprise environments.
• Predictable delivery with reduced risk, improved alignment, and high stakeholder satisfaction.
• Ability to run dual projects efficiently while maintaining deep technical influence.