AI/ML Practice Architect

Position Overview

The AI/ML Practice Architect is a senior, hands-on technical leader who combines solution architecture and customer consultation with deep applied AI/ML engineering capability. This role accelerates practice growth by shaping repeatable offerings, guiding delivery excellence, and building production-grade AI systems - especially GenAI/LLM, retrieval, evaluation, and agentic workflows - integrated into customer platforms.

This role will travel as needed, which includes customer onsite workshops, executive presentations, delivery kickoffs, and internal practice events.

Why This Role Exists

• Drive measurable customer outcomes by designing and delivering AI/ML and GenAI solutions end-to-end (discovery, build, deploy, operate).
• Scale TGS AI capabilities through reusable playbooks, reference architectures, accelerators, and enablement.
• Partner with Sales and Delivery to shape, price, and win work; serve as a trusted advisor in pre- and post-sales engagements.

Key Outcomes (First 6-12 Months)

1. Establish (or evolve) the AI/ML Solution Playbook: discovery templates, context/RAG patterns, evaluation rubrics, and governance/guardrails.
2. Deliver 2-4 production deployments (or major releases) demonstrating reliability, safety, and business value; publish reusable artifacts to the practice repository.
3. Create a reference architecture for agentic AI/LLM systems (tool use, memory, orchestration, human-in-the-loop controls, observability).
4. Improve delivery predictability: clear estimation models, quality gates, and SDLC/MLOps/LLMOps standards aligned to DevOps principles.
5. Support GTM: contribute to proposals, SOWs, pricing, case studies, demos, and executive narratives that help land-and-expand accounts.

Core Responsibilities

A) Practice Architecture & Consulting (Customer Value + Growth)

• Lead customer discovery and value definition: map current/future-state workflows, define success metrics, and translate business goals into technical requirements.
• Design solution architectures and delivery approaches; document assumptions, risks, dependencies, and cost/effort estimates.
• Create and maintain practice solution content: reference architectures, accelerators, templates, delivery playbooks, and pricing guidance.
• Partner with Sales, Solutioning, Delivery Leadership, and Practice Directors on pre-sales strategy, proposals, and executive communications.
• Mentor consultants/engineers: design reviews, technical coaching, and best-practice enablement across engagements.
• Continuously optimize delivery processes, promote reuse, and champion innovation rooted in measurable customer impact.

B) AI/ML Engineering & Delivery (Hands-on Technical Leadership)

• Build and ship production AI/ML systems including model integration, data pipelines, services, and user experiences.
• Design and implement GenAI/LLM solutions: prompt & context engineering, RAG grounding, embedding/vector store strategies, and latency/quality trade-offs.
• Evolve prompted workflows into agentic AI: durable execution, tool use, memory, orchestration (single- and multi-agent), and human-in-the-loop gates.
• Establish evaluation and experimentation rigor: offline/online tests, human + AI evaluation rubrics, error taxonomies, and KPI instrumentation.
• Implement security, safety, and compliance controls: PII handling, prompt-injection mitigations, model risk management, and red-teaming.
• Operationalize MLOps/LLMOps: CI/CD, model/version management, observability, drift/feedback loops, and incident response.

C) Leadership, Accountability & Cross-Functional Influence

• Drive execution cadence with clear gates, owners, and KPIs from discovery through launch.
• Hold self and others accountable to commitments; proactively resolve issues before they impact customers.
• Communicate trade-offs, impact, and risk to technical and non-technical stakeholders; produce executive-ready narratives and artifacts.
• Support practice hiring/interviewing and capability building as needed; contribute to internal communities of practice.

Required Qualifications

• 8 or more years in full stack software engineering, data engineering, ML engineering, or applied AI, including delivery in customer-facing or consulting environments.
• 3 or more years building and deploying ML/LLM/GenAI-powered products (e.g., prompt/context engineering, RAG, evaluation, and guardrails).
• Strong solution architecture skills: requirements gathering, estimation, system design, and leading technical decisions across teams.
• Proficiency in Python and at least one of: TypeScript/JavaScript, Java, Go, or Rust; experience building APIs and services (e.g., FastAPI, Django, Node/Express).
• Experience with cloud platforms (AWS, Azure, or Google Cloud Platform) and containerization (Docker); familiarity with Kubernetes is a plus.
• Ability to influence across stakeholders (Sales, Delivery, Engineering, Product, Security/Privacy) and communicate effectively at multiple levels.

Preferred Qualifications

• Experience deploying or fine-tuning open-source LLMs (e.g., Hugging Face, vLLM, Ollama) and/or using managed services (e.g., Azure OpenAI, Bedrock, Vertex AI).
• Hands-on experience with agent frameworks/orchestration (e.g., LangGraph, AutoGen, CrewAI) and tool integration patterns.
• Experience with vector databases and semantic search (e.g., Pinecone, Weaviate, Chroma) and advanced RAG/knowledge graph techniques.
• Experience leading small teams or technical workstreams while remaining hands-on.
• Exposure to regulated environments (e.g., Finance & Healthcare) and formal security/privacy review processes.

Job Details

• Location: Chicago, IL (Fully Remote)
• Pay Range: $136,000 - $204,000/yr
• Application Deadline: Jun 12, 2026