Job description

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.

AI/LLM Technical Architecture

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.

Program & Technical Oversight

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.

Experience and Qualifications

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.

Preferred 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.

What This Role Enables

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.

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