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About This Role
Keys to Success:
- 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.
Primary Focus:
Lead the design and implementation of Infrastructure Automation systems across client engagements, including agent orchestration, tool/function use, retrieval\-augmented generation (RAG), evaluation, guardrails, and production deployment.
Key Technical Skills:
- Networking: Enterprise routing/switching, multi\-vendor (Cisco, Arista, Juniper)
- Infrastructure\-as\-Code: NetBox, Git/YAML, Jinja2 templates, version control
- Automation \& APIs: Python (API automation, scripting), Ansible, Terraform, orchestration tools (Itential, StackStorm, AWX)
- Integration \& APIs: REST/GraphQL, secrets management (Vault/CyberArk), IPAM (Infoblox/NetBox), artifact repositories
- Testing \& Validation: Robot Framework, pytest, pyATS; test\-driven automation
- DevOps Practices: CI/CD, containerization (Docker/Kubernetes/OpenShift), GitOps, documentation\-as\-code, observability
- 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).
Preferred Experience:
- 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).
Soft Skills:
- Excellent documentation and knowledge\-sharing skills.
- Strong cross\-team collaboration and communication.
- Self\-driven problem solver; comfortable with ambiguity, which is a must in a rapidly evolving AI landscape.
- Ability to convert business requirements into agentic AI solutions with clear ROI.
Qualifications:
- 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.
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $117,000\-$146,000 annually. Actual compensation will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base pay.
The well\-being of WWT employees is essential. So, when it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full\-time employees:
- Health and Wellbeing: Health, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program
- Financial Benefits: Competitive pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Tuition Reimbursement
- Paid Time Off: PTO and Sick Leave (starting at 20 days per year) \& Holidays (10 per year), Parental Leave, Military Leave, Bereavement
- Additional Perks: Nursing Mothers Benefits, Voluntary Legal, Pet Insurance, Employee Discount Program
We strive to create an environment where all employees are empowered to succeed based on their skills, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for All!
If you have any questions or concerns about this posting, please email
taposting@wwt.com.
\#LI\-WWTACRIDER
Requirements:
Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.
Founded in 1990, WWT brings together strategy, deep technical expertise and world\-class partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)—a collaborative ecosystem featuring state\-of\-the\-art hardware and software—WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distributions capabilities.
With more than 14,000 team members and over 60 locations globally, WWT's culture—grounded in core values and leadership philosophies—has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
What is the Solutions Consulting \& Engineering (SC\&E) Team and why join?
Solutions Consulting \& Engineering is an organization that is Customer Focused and Solutions Led. We deliver end\-to\-end and emerging solutions to drive customer satisfaction, increase profitability and growth. Our success is enabled by our world\-class management consulting, delivery excellence and engineering brilliance. Our goal is to bring together business acumen with full\-stack technical know\-how to develop innovative solutions for our clients' most complex challenges, including the next frontier: agentic AI systems that reason, plan, and act on behalf of the enterprise.
Position Overview:
As a Sr. Infrastructure AI Automation 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 Automation 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.
Key Responsibilities:
Customer Focus
- Understand customer needs and design agentic AI solutions that solve for both their short\-term and long\-term needs.
### Creative Solutioning
- Understand customer problems and develop novel agentic solutions that are differentiated from our competitors, combining foundation models, retrieval, orchestration, and enterprise context.
### Strategic Client Relationship Management
- 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.
### Commercial Success
- Build strong customer relationships and deliver customer\-centric agentic AI solutions that produce measurable value.
### End\-to\-End Solution Management \& Delivery
- 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.
Salary Context
This $117K-$146K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At World Wide Technology, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($131K) sits 40% below the category median. Disclosed range: $117K to $146K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
World Wide Technology AI Hiring
World Wide Technology has 31 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, Hartford, CT, US, St. Louis, MO, US. Compensation range: $104K - $300K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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