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About This Role
This is a hybrid role*
About Infinitive
Infinitive is a data and AI consultancy that helps clients modernize, monetize, and operationalize their data to generate lasting value. They pride themselves on their deep industry and technology expertise, ensuring that they drive and sustain the adoption of new capabilities. Infinitive is committed to aligning their team with their clients' culture, ensuring a successful partnership by bringing the right mix of talent and skills for high return on investment.
Infinitive has earned recognition as one of the "Best Small Firms to Work For" by Consulting Magazine, receiving this accolade nine times, most recently in 2026. They have also been honored as a “Top Workplace” by the Washington Post, “Best Places to Work” by the Washington Business Journal, and “Best Places to Work” by Virginia Business.
The Role
The AI Transformation Engineer helps clients rethink how work should be performed when Large Language Models, agents, automation, data, and modern AI development tools are available. The role combines business process understanding, critical thinking, AI engineering, data, and human centered transformation.
The successful candidate can start with an ambiguous challenge and systematically define the required outcome, information, decisions, prompts, context, tools, controls, validation, and human responsibilities needed to deliver it reliably.
What You Will Do:
Business Process Transformation
- Lead discovery with executives, business teams, subject matter experts, analysts, and engineers.
- Decompose complex processes into steps, decisions, inputs, outputs, rules, dependencies, and exceptions.
- Identify bottlenecks, repetitive work, knowledge gaps, and inefficient handoffs.
- Challenge legacy process assumptions and design future state workflows around AI enabled capabilities.
- Create practical roadmaps that connect prototypes to scalable operating models.
Prompt and Context Engineering
- Design system prompts, task prompts, reusable instructions, and multi step prompt workflows.
- Translate requirements, policies, and expert knowledge into explicit instructions, constraints, examples, and escalation conditions.
- Define structured output formats that people and downstream systems can consume reliably.
- Determine what context should be persistent, retrieved dynamically, or supplied by the user.
- Optimize model context for quality, speed, cost, and maintainability.
MCP Tools and Handler Engineering
- Design MCP servers, clients, and reusable tools where they improve access to enterprise capabilities.
- Create clear tool descriptions, efficient parameters, structured responses, and predictable handler behavior.
- Connect AI applications with APIs, databases, applications, knowledge repositories, and data platforms.
- Plan permissions, authentication, governance, logging, error handling, and recovery.
- Define when AI may act, when it should recommend, and when a human must approve.
Markdown and Knowledge Architecture
- Create AGENTS.md, CLAUDE.md, README.md, business rules, prompt libraries, process definitions, data dictionaries, tool documentation, architecture notes, and evaluation cases.
- Organize large amounts of information so both humans and AI can navigate it efficiently.
- Use hierarchy, references, examples, rules, and exceptions to create durable and reusable context.
- Manage cross document relationships and reduce unnecessary token consumption.
AI Agents and Workflow Engineering
- Design agentic workflows that can plan, retrieve, reason, call tools, and request input.
- Define authorization boundaries, memory and state, failure recovery, escalation, and human checkpoints.
- Develop reusable components that support multiple use cases.
- Use modern AI tools to prototype, document, test, troubleshoot, and accelerate implementation.
AI Evaluation and Quality
- Define measurable acceptance criteria and representative evaluation datasets.
- Test accuracy, consistency, unsupported claims, latency, token use, and cost.
- Compare models, prompting strategies, tool designs, and workflow alternatives.
- Analyze failures across the model, prompt, context, data, tool, workflow, and process layers.
- Build feedback loops that improve the solution over time.
Critical Thinking Is the Core Capability
AI models and tools will continue to change. The enduring capability for this role is the ability to structure difficult problems, test assumptions, and select the simplest reliable approach. Strong candidates naturally ask questions such as:
- What outcome are we actually trying to achieve?
- Why does this process exist and which steps create value?
- What information is required and is it trustworthy?
- Which decisions are deterministic and which require judgment?
- What assumptions are we making?
- How should the system behave when evidence is incomplete or contradictory?
- What actions can AI safely perform?
- What is the simplest architecture capable of producing the required outcome?
Preferred Experience
Strong candidates may come from consulting, business analysis, process engineering, product management, solution architecture, software engineering, data engineering, automation, or operations. Experience with several of the following is preferred:
- Large Language Models, agents, and AI assisted development
- Prompt engineering and context engineering
- MCP servers, clients, tools, and handlers
- Function calling, APIs, and integration design
- RAG, vector search, and semantic retrieval
- Python, SQL, JSON, YAML, Markdown, and Git
- Databricks, cloud, or enterprise data platforms
- Business transformation, consulting, architecture, process, product, data, or software engineering
Qualifications
- Strong analytical and critical thinking skills.
- Ability to break complex problems into understandable components.
- Hands on experience with generative AI and modern AI development tools.
- Ability to translate business concepts into technical instructions and workflows.
- Excellent Markdown, documentation, and written communication skills.
- Understanding of APIs, tools, integrations, structured data, and testing.
- Ability to collaborate across business and technology teams.
- Comfort operating in ambiguity and continuously learning.
Example Engagement
A team spends hours reviewing customer requests, policies, transaction data, and historical cases before making a recommendation. The AI Transformation Engineer redesigns the process, defines the data and tools required, creates prompts and durable Markdown instructions, and establishes validation and human decision points.
Request Gather Context Retrieve Data Apply Rules Analyze Recommend Human Review Execute Learn
What Success Looks Like
Success is measured by better business and engineering outcomes, not by the number of prompts, models, or prototypes produced. Expected results may include:
- Faster and more reliable business processes
- Reduced manual work and process complexity
- More consistent analysis and decisions
- Faster delivery of AI enabled products and services
- Better employee and customer experiences
- Improved data utilization and knowledge access
- Lower operating cost with appropriate controls
- Organizations operating differently because of AI
Why Infinitive
AI is changing how organizations operate, develop software, use data, manage knowledge, and serve customers. At Infinitive, you will work directly with clients on meaningful business challenges, experiment with emerging technologies, and help move organizations from isolated AI experiments to reliable AI enabled operations.
We are looking for people who can combine sound judgment, business understanding, technical curiosity, and disciplined execution to help define how people and AI work together.
Infinitive is required by law in some jurisdictions to include a reasonable estimate of the compensation range for this role. The determination of this range includes various factors not limited to skill set, level, experience, relevant training, and licensure and certifications. Compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range for this role in the U.S. is $90,000 - $154,00.00.
Infinitive is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.
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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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Infinitive Inc, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Infinitive Inc AI Hiring
Infinitive Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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