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.
Responsibilities
Lead discovery sessions with executives and business teams.
Decompose complex processes into meaningful steps and decisions.
Identify process bottlenecks and inefficiencies.
Challenge legacy assumptions to design future workflows.
Create practical roadmaps linking prototypes to scalable models.
Design system prompts and reusable instructions.
Plan governance and error handling for AI applications.
Organize knowledge documents for efficient navigation.
Benefits
Work on meaningful business challenges.
Opportunity to experiment with emerging technologies.
Collaboration with diverse teams on AI integration.
Contribute to transforming organizations with AI.
A culture that emphasizes sound judgment and technical curiosity.
Full Job Description
***This is a hybrid role
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.
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.
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.
