AI​/ML Practice Architect

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise.

We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunities for people to achieve fulfillment through career success.

Ready to join us?

Here’s what the opportunity supported through our TGS Talent Acquisition Team requires:

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 Dev Ops 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,…