AI/ML Practice Architect

AI/ML Practice Architect Skills

• IT Strategy
• Customer Experience
• Business Intelligence
• eXist
• Presentations
• Software Development Methodology
• DevOps
• Google Tag Manager (GTM)
• Pricing
• Sales Strategy
• Executive Communications
• Mentorship
• Engineering Design
• Coaching
• IT Management
• Workflow
• Instrumentation
• Regulatory Compliance
• Machine Learning Operations (ML Ops)
• Continuous Integration
• Continuous Delivery
• Version Control
• Incident Management
• Leadership
• Cadence
• KPI
• Interviewing
• Software Engineering
• Data Engineering
• Customer Facing
• Evaluation
• Solution Architecture
• Requirements Elicitation
• Estimating
• Systems Design
• Python
• TypeScript
• JavaScript
• Java
• Rust
• Django
• Cloud Computing
• Amazon Web Services
• Google Cloud Platform
• Google Cloud
• Docker
• Kubernetes
• Open Source
• Managed Services
• Microsoft Azure
• Vertex
• Orchestration
• Autogen
• Vector Databases
• Semantic Search
• Finance
• Health Care
• Privacy
• Customer Focus
• Accountability
• Innovation
• Continuous Improvement
• Machine Learning (ML)
• Generative Artificial Intelligence (AI)
• Sales
• Risk Management
• Adaptability
• Product Management
• Communication
• Taxes
• Life Insurance
• Partnership
• Collaboration
• Business Transformation
• Law
• Sourcing
• Screening
• Recruiting
• Artificial Intelligence
• Summary Description 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 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 Pl... Visit the Employer site for more details