AI Developer

WittKieffer

1d•Hybrid

About The Position

WittKieffer is a mission-driven firm focused on advancing wellbeing across the Quality of Life ecosystem through impactful leadership. This AI Solutions Developer role is designed to transform business processes by applying AI technologies, bridging modern generative and agentic AI capabilities with real-world business challenges. The goal is to drive rapid, usable improvements and measurable outcomes across all business operations. This is an early-career, builder-focused position for a software/AI developer with 0-3 years of experience, who is curious, learns quickly, and enjoys rapid experimentation and iteration. The AI Solutions Developer will collaborate closely with the IT team, consultants, operations leaders, support teams, and enterprise leaders to translate needs into working AI-powered agents, automations, and workflows, emphasizing the use of existing enterprise toolsets and platforms. WittKieffer offers a full benefits package and is proud to be 100% employee-owned, working exclusively with organizations in not-for-profit and for-profit healthcare, life science, and education.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or related field.
Foundational software engineering skills and API integration experience.
Familiarity with and initial training with AI agents, prompts, RAG, and workflow orchestration.
Working knowledge of data access and integration (SQL, APIs, Graph, MCP, data pipelines) and responsible data handling (privacy, security, and governance).
Working knowledge of agents, registration/registries, and orchestration platforms.
Ability to use existing tools to their fullest potential, while recommending and advocating for proper tooling for future growth and potential.
Ability to take experiments to delivery: deploy services/apps, build CI/CD pipelines, and implement observability (logging/metrics/tracing) plus cost/latency controls.
High levels of curiosity and learning mindset; ability to leverage knowledge and curiosity to define objectives, research approaches, and develop recommendations to solve for desired outcomes.
Strong collaboration skills, with the ability to translate business needs into technical designs and incremental deliverables.
Able to work independently while demonstrating high degrees of accountability, proactive communication, and resiliency.

Nice To Haves

Familiarity with MLOps/LLMops practices (model/prompt versioning, experiment tracking, offline/online evaluation, monitoring for drift/quality).
Exposure to enterprise AI approaches and platforms: Microsoft Azure, Foundry, Agent Framework, Fabric, CoPilot Studio, and GitHub Actions, utilizing Open standards and Interoperability (MCP, A2A, etc.).
Exposure to modern development practices (version control, code reviews, unit/integration testing, packaging, and documentation).
Exposure to building and integrating generative and agentic AI solutions (LLM APIs/SDKs, prompt/agent frameworks, and retrieval-augmented generation with vector search).

Responsibilities

Build and maintain hands-on expertise in AI capabilities by prototyping agents, automations, and AI‑first workflows using modern frameworks, models, and developer tools.
Design, build, test, and refine AI solutions in code (prompts, agents, RAG workflows, models and MCP integrations) and package them into reusable components and services.
Rapidly iterate from idea to working prototype to deployable solution, running experiment cycles end-to-end: define hypotheses, implement prototypes, create evaluation datasets, measure quality/cost/latency, and iterate using repeatable testing and dashboards.
Perform technical and process discovery (data access, API/system constraints, security/privacy, and operational workflows) and recommend practical designs that are feasible to implement and support.
Assist in with production readiness, including CI/CD pipelines, secure code approach and practices, compliance and risk review, monitoring, and runbooks.
Create clear documentation (architecture, setup, prompt/agent specs, evaluation approach) and enablement materials to support adoption, maintenance, and handoff.