Role Description 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) - Establish (or evolve) the AI/ML Solution Playbook: discovery templates, context/RAG patterns, evaluation rubrics, and governance/guardrails. - Deliver 2–4 production deployments (or major releases) demonstrating reliability, safety, and business value; publish reusable artifacts to the practice repository. - Create a reference architecture for agentic AI/LLM systems (tool use, memory, orchestration, human-in-the-loop controls, observability). - Improve delivery predictability: clear estimation models, quality gates, and SDLC/MLOps/LLMOps standards aligned to DevOps principles. - Support GTM: contribute to proposals, SOWs, pricing, case studies, demos, and executive narratives that help land-and-expand accounts. Core Responsibilities - 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. - 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. - 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. 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 GCP) 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. Success Profile (Competencies & Behaviors) - Manages Complexity: Integrates diverse inputs, clarifies ambiguity, and translates business and technical constraints into actionable architecture and delivery plans. - Customer Focus: Builds trusted relationships, aligns to measurable outcomes, and delivers customer-centric solutions with quantified impact. - Communicates Effectively: Delivers clear multimode communication; explains trade-offs, risks, and “the why” behind decisions to technical and non-technical audiences. - Ensures Accountability: Owns outcomes, meets commitments, escalates early, and drives execution to closure. - Cultivates Innovation: Creates new and better ways for the organization to be successful; promotes reuse and continuous improvement. - Optimizes Work Processes: Designs efficient delivery processes; improves quality through automation, standards, and playbooks. - Nimble Learning / Self-Development: Learns through experimentation, seeks feedback, and stays current with AI/ML and GenAI trends. - Collaborates: Builds partnerships across Sales, Delivery, Engineering, and customer teams to meet shared objectives. - Decision Quality: Makes good and timely decisions using decision criteria, trade-offs, and risk mitigation. - Manages Ambiguity / Situational Adaptability: Operates effectively when the path is unclear; adapts approach to shifting constraints and stakeholder needs. - Drives Results: Consistently delivers results under tough circumstances and focuses on key goals during setbacks. Job Type & Location This is a Permanent position based out of Chicago, IL. Pay and Benefits The pay range for this position is $136,000.00 - $204,000.00/yr. We reserve the right to pay above or below the posted wage based on factors unrelated to sex, race, or any other protected classification. - Medical, Dental, and Vision - Critical Illness, Accident, and Hospital - 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available - Life Insurance (Voluntary Life and AD&D for employee and dependents) - Short and Long-Term Disability - Health Spending Account (HSA) - Transportation Benefits - Employee Assistance Program - Time Off/Leave (PTO, Vacation or Sick Leave) Workplace Type This is a fully remote position. Application Deadline This position is anticipated to close on Jun 12, 2026.