Applied AI Engineer

AIM's Digital Product Engineering & Platforms practice helps clients turn prioritized AI opportunities into working, defensible solutions. The Applied AI Solutions Engineer is the person who takes a use case, scopes and estimates the solution, and builds the proof, across generative AI, hyperautomation, and enough classic machine learning to size a data-science effort even when someone else ultimately builds the model.

This is a solutioning role first and a building role second, the two are inseparable here. You'll design a structured, defensible feasibility test (not a demo), build a proof of concept, and produce a recommendation leadership can act on, complete with a real cost-per-unit estimate. You'll design intelligent automation that removes manual hand-offs across systems. And you'll be the technical voice in front of the client who can say what a solution takes to build, what it costs to run, and where the risk is.

You'll work across two kinds of engagements: focused build-feasibility projects (evaluate models, prove a concept, estimate the build) and broader AI-enablement programs (assess technical feasibility and data readiness across a portfolio of candidate use cases, then shape and size solutions for the ones worth doing).

Core Responsibilities:

Feasibility & POC:

• Own LLM and vision/multimodal feasibility analyses: evaluate and compare third-party models for a specific use case, run structured tests against real data, and produce accuracy findings, a cost-per-unit estimate, and a clear recommendation.
• Translate prioritized use cases into solution architectures and phased effort estimates; contribute technical inputs to build estimation for current- and future-phase work.
• Assess technical feasibility and data readiness across candidate use cases on enablement engagements.

Build:

• Build POCs/MVPs and intelligent automation - LLM applications, RPA/workflow orchestration, document processing, integrations, and agentic workflows - within the client's technical environment.

Governance & communication:

• Contribute AI/ML findings and risk framing to governance and compliance assessments.
• Present feasibility findings, model recommendations, and cost estimates at gate readouts.

Must Haves:

• 5–7+ years building applied software, data, or automation solutions, with recent hands-on AI/LLM work.
• Solution definition & estimation: can translate an ambiguous business use case into a solution architecture, an effort estimate, and a phased plan. This is the core of the role: you scope and size, not just build.
• Strong Python and direct, hands-on experience with LLM provider APIs (Anthropic / Claude, OpenAI, or comparable), including prompt engineering, structured output, and evaluation of model outputs against real documents and data.
• Proven ability to design a structured, defensible feasibility test - comparing multiple third-party models for a specific use case and producing a clear cost-per-unit / cost-per-document estimate and a model recommendation, not a one-off demo.
• Hyperautomation - can design and build intelligent automation: RPA (UiPath or comparable), workflow / BPA orchestration, intelligent document processing, system integration via APIs, and agentic workflows that chain steps across enterprise systems (CRM, ERP, SaaS).
• Machine-learning literacy sufficient to scope and estimate - understands supervised vs. unsupervised learning, classification / regression / forecasting, computer vision, evaluation methodology, and data-readiness requirements well enough to define, size, and de-risk an ML solution or project, even when a data scientist executes the build.
• Fluent in AI economics. Token pricing mechanics (input / output / cached tokens), cost-per-transaction modeling, model routing and tiering, prompt/context optimization, caching, and self-host-vs-API trade-offs - plus a point of view on why inference cost and the shift to usage-metered licensing have become top budget lines for enterprise clients.
• Clear technical communication to both engineers and non-technical executives - able to explain model capability, limitations, cost, and recommendation to either audience.
• Client-facing consulting or embedded partner-delivery experience.

What Good Looks Like Early:

• Takes a messy real-world use case (e.g., unstructured documents in many formats) and returns a feasibility finding with accuracy, cost, and a go / no-go recommendation.
• Can whiteboard a solution across the complexity spectrum, from an agent/skill to an agentic integration to a custom ML build, and put a credible effort range on each.
• Builds a working POC against real data, then tells leadership what it would cost to run at production volume.
• Flags the data-readiness or cost problem that would sink a use case before the client spends money on it.

Nice to Haves:

• Computer vision / multimodal model evaluation - image-based hazard, defect, or object detection.
• Experience productionizing an LLM proof of concept into a scaled application (not just a demo), including evals, monitoring, and drift.
• Cloud experience (AWS / Azure / GCP; Bedrock / Vertex / Azure OpenAI) and comfort with private-cloud or on-prem inference constraints (e.g., Nutanix).
• RAG and vector stores (pgvector, Pinecone) and agent frameworks (LangGraph, Claude Agent SDK, n8n, Copilot Studio).
• Familiarity with AI governance, model auditability, and responsible AI in regulated environments.
• Industry context in manufacturing, industrials, skilled trades / blue-collar, retail, or professional services - not required, but a meaningful plus.

We are a diverse group of individuals.

No two people, ideologies, or thoughts are the same. Our different experiences and perspectives are our strengths. We are passionate about seeing each other succeed and live & breathe our company values; we choose positivity, we take ownership, we are relationship driven, we build trust, and we are self-aware. We work hard to come through for our clients, and for one another. We are many unique people, with the same common goal in mind – to connect, inspire and empower our customers by leveraging an amazing workforce to help solve business challenges, drive innovation and produce results that exceed expectations. Simply put, we are more than a company; we are our client's trusted advisors.

AIM Consulting is an Equal Opportunity Employer.

AIM Consulting is an Equal Opportunity Employer. AIM Consulting provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws. AIM Consulting complies with applicable state and local laws governing non-discrimination in employment in every location in which the company has facilities. Reasonable accommodation is available for qualified individuals with disabilities, upon request.

At AIM Consulting, we value people from all walks of life. We understand not everyone will meet all the above qualifications on day one, and that’s okay. We are heavily invested in further education and training, and we are committed to building a diverse, inclusive and equitable workplace where you can show up as your true self. If you're passionate about technology, but your previous experience doesn't perfectly align with every qualification listed, we still encourage you to apply.