Sr. Agentic AI Consultant

World Wide Technology Healthcare Solutions•Jenks, OK

1d•Hybrid

About The Position

As a Sr. Agentic AI Consultant, you will take a leadership role in delivering agentic AI outcomes across a wide range of global clients. You will engage with customer leadership to define priorities and outcomes, expand the capabilities and offerings of the Automation practice, and lead consulting engagements that bring autonomous, tool-using AI agents into production. You'll develop AI strategies, design multi-agent architectures, and lead build efforts by orchestrating large language models (LLMs), retrieval systems, tool integrations, and human-in-the-loop controls, all while using your communication skills to provide leadership and guidance to clients and engineers. Your consultative approach will help you evaluate client requirements, propose agentic solutions, and achieve measurable business objectives. You'll mentor engineers and collaborate with sales account executives, technology partners, and client IT and business executives.

Requirements

You have a model-first, programmability-first mindset. You think in prompts, tools, and agent graphs as naturally as in code.
You focus on understanding data and curating powerful evaluations, including offline evals, online telemetry, and human feedback loops.
You excel at thoroughly documenting and communicating ideas to a broad audience, including non-technical business stakeholders.
Your attention to detail and code-craft is unparalleled. You care about what the agent actually does in the real world, not just the happy path.
You communicate and think in a structured manner.
You're comfortable engaging with clients, partners, peers, and anyone who has valuable input.
You have a passion for helping others achieve success.
You have domain expertise across multiple areas such as software development, cloud, data engineering, machine learning / LLMs, and enterprise integration.
Agent Frameworks & Orchestration: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, Claude Agent SDK, OpenAI Agents SDK; multi-agent coordination and hand-off patterns.
Foundation Models & LLM Platforms: Anthropic Claude, OpenAI, Google Gemini, Meta Llama, Mistral; AWS Bedrock, Azure OpenAI, Google Vertex AI; model selection, routing, and cost/latency optimization.
Tool Use & Integration: Function calling, structured outputs, Model Context Protocol (MCP) servers and connectors, REST/GraphQL APIs, webhooks, enterprise identity (OAuth/SAML).
Retrieval & Knowledge: RAG architectures, vector databases (Pinecone, Weaviate, pgvector, Chroma, Milvus), embeddings, hybrid search, reranking, chunking strategies, knowledge graphs.
Prompt Engineering: Systematic prompting patterns (ReAct, reflection, planner/executor), prompt versioning, context engineering, few-shot design.
Evaluation & Observability: LangSmith, Langfuse, Arize, Weights & Biases, Braintrust; agent tracing, eval harnesses (golden sets, LLM-as-judge), A/B testing, human feedback loops.
Responsible AI & Guardrails: NeMo Guardrails, Llama Guard, input/output filtering, PII detection, prompt-injection defense, policy enforcement, audit logging.
Engineering: Python (primary), TypeScript/Node; Git/YAML, version control; test-driven development with pytest.
LLMOps / DevOps: CI/CD, containerization (Docker/Kubernetes/OpenShift), GitOps, prompt & model versioning, documentation-as-code, secrets management (Vault/CyberArk).
Ability to perform concurrent tasks in complex environments under adjusting priorities.
Ability to communicate and modify approach, language, and style to different audiences, including C-suite executives.
Professional writing style and experience with demonstrable technical and business-related artifacts is required.
Collaborative, with the ability to manage conflicting interests and deal with ambiguity.
Effective communication skills: capable of supporting presentations to convey concepts and solutions, writing effective emails, and discussing AI strategy with senior executives.
Strong teamwork qualities: able to gain the trust of customers and collaborate effectively within the WWT team.
Intellectually curious with a desire to continuously track advances in foundation models, agent research, and the broader AI ecosystem.
Proactive, collaborative, with emotional intelligence, and the capacity to learn and synthesize new information rapidly.
Adaptable, with the ability to conform to shifting priorities, demands, and timelines through analytical and problem-solving capabilities.
Self-directed, with the ability to adapt to change and competing demands.
You have extensive experience in designing, building, and deploying AI or intelligent-automation solutions within an organization.
You hold a bachelor's degree in Computer Science, Electrical Engineering, Data Science, or have equivalent experience.
You have a proven track record of leading large and complex AI or agentic automation engagements.
You have experience in developing standards and best practices for AI development projects, including prompt, evaluation, and deployment standards.
You are familiar with modern development tools and environments, such as Git, Visual Studio Code, Docker/Podman, Kubernetes/OpenShift, and Linux/Unix.
You have experience with data serialization formats such as JSON, YAML, XML, and CSV.
You have proficiency in Python; experience with TypeScript, Go, or Rust is a plus.
You have a solid understanding of LLM APIs, function/tool calling, streaming, structured outputs, and token economics.
You have foundational knowledge of public cloud AI platforms such as AWS Bedrock, Azure OpenAI, and Google Vertex AI, and experience integrating with on-premises or private model deployments (vLLM, TGI, Ollama, NVIDIA NIM).
You have working knowledge of one or more agent frameworks and platforms, such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or the Claude Agent SDK.
You have working knowledge of one or more retrieval and vector-database platforms, such as Pinecone, Weaviate, pgvector, Chroma, or Milvus.
You have working knowledge of agent evaluation and observability tooling, such as LangSmith, Langfuse, Arize, or Braintrust.

Nice To Haves

Deploying and scaling agentic AI solutions in production enterprise environments.
Designing multi-agent systems with planner/executor, critic, and human-in-the-loop patterns.
Architecting model-agnostic orchestration layers and abstraction frameworks across providers.
Building agent evaluation and observability pipelines, including eval-driven development workflows.
Integrating agentic workflows with enterprise systems (ServiceNow, Jira, Salesforce, M365, ITSM platforms).
Fine-tuning, LoRA/adapters, distillation, and model-routing strategies for cost and performance.
Enterprise AI governance, responsible AI frameworks, and compliance (NIST AI RMF, EU AI Act awareness).

Responsibilities

Understand customer needs and design agentic AI solutions that solve for both their short-term and long-term needs.
Understand customer problems and develop novel agentic solutions that are differentiated from our competitors, combining foundation models, retrieval, orchestration, and enterprise context.
Use a strategic approach to managing customer interactions and data throughout the customer journey, with a goal of higher business growth through better customer experiences powered by AI.
Build strong customer relationships and deliver customer-centric agentic AI solutions that produce measurable value.
Lead client agentic AI engagements with a focus on strategy, enablement, and execution, from discovery and use-case qualification through design, build, evaluation, and production rollout.
Provide technical leadership during client engagements across model selection, agent design, tool/function integration, retrieval architecture, and evaluation strategy.
Define agentic AI architectures and designs that are simple, effective, and responsibly governed.
Mentor and collaborate with peers to raise the bar for everyone, including yourself.
Drive continuous improvement within the practice through development, collaboration, and mentorship.
Contribute to the practice as a "librarian" of agentic AI intellectual property, including reusable agents, prompt libraries, evaluation harnesses, and reference architectures.