Sr. Engineer AI

Medline•Northbrook, IL

20h

About The Position

The Senior AI Engineer is a hands-on AI, data, analytics, and automation practitioner embedded within a business domain such as Commercial, Operations, Product, or Corporate Functions. The role identifies, designs, prototypes, and delivers practical solutions that drive measurable business value. The Senior AI Engineer works directly with business teams to understand workflows, decisions, pain points, and opportunities, then rapidly translates them into working proofs of concept, pilots, demonstrations, automations, agents, copilots, analytics solutions, and decision-support tools. This is a hands-on builder role. The Senior AI Engineer is expected to use modern AI-assisted development tools and approved enterprise platforms to create solutions directly, not simply define requirements or coordinate delivery. The role owns outcomes, not tickets. The Senior AI Engineer selects the simplest effective approach. A solution may use process improvement, traditional workflow automation or RPA, analytics, machine learning, generative AI, or a combination of these approaches. The role determines what can be delivered directly and what should transition to engineering, data science, platform, product, or automation teams for production deployment and scale. The role serves as the bridge between business stakeholders and technical teams, combining strong communication, business understanding, product thinking, technical problem solving, and hands-on execution. The Senior AI Engineer remains engaged through testing, transition, adoption, and measurable value realization.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Statistics, Analytics, or a related field, or equivalent practical experience.
5+ years of experience in AI/ML engineering, data science, data engineering, analytics engineering, automation engineering, solution engineering, or a related technical discipline.
Demonstrated experience building and delivering AI, analytics, data, or automation solutions that address real business problems and are used in business workflows.
Experience personally creating proofs of concept, prototypes, pilots, demonstrations, or minimum viable solutions.
Experience working directly with business stakeholders and end users to translate ambiguous needs into practical solutions.
Experience moving solutions from discovery and prototype through pilot, deployment, or transition to a production delivery team.
Strong hands-on Python and SQL skills.
Ability to independently build AI applications, workflow automations, RPA solutions, analytics solutions, data workflows, integrations, APIs, and decision-support tools.
Working knowledge of generative AI, large language models, prompt engineering, retrieval-augmented generation, agents, model evaluation, APIs, data integration, and workflow automation.
Solid machine learning, statistics, experimentation, forecasting, and optimization fundamentals.
Working knowledge of version control, testing, CI/CD, monitoring, documentation, and secure data handling.
Ability to rapidly learn unfamiliar business domains, workflows, systems, data, and technology platforms.
Strong product mindset with a focus on user needs, adoption, measurable value, and iterative delivery.
Ability to communicate clearly with technical and non-technical audiences and influence across organizational boundaries.
Ability to balance rapid experimentation with enterprise architecture, security, data governance, privacy, and Responsible AI requirements.
Sound judgment on when to build directly, when to use low-code or RPA, and when to transition execution to specialized teams.
Bias for action, hands-on problem solving, and ownership from idea through outcome.

Nice To Haves

Master's degree in Computer Science, Engineering, Data Science, Analytics, Business Administration, or a related field.
Relevant cloud, AI, data, analytics, automation, agile, or product certifications preferred.
Experience building generative AI applications, agents, copilots, RAG solutions, intelligent workflow automations, RPA solutions, analytics products, forecasting models, optimization tools, or decision-support solutions.
Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Fabric, Databricks, Azure Machine Learning, Power Platform, Power Automate, Copilot Studio, or comparable enterprise platforms.
Experience in forward deployed engineering, solution engineering, technical consulting, analytics engineering, automation engineering, or another business-facing technical role.
Experience integrating enterprise systems, APIs, data platforms, analytics platforms, automation tools, and business applications.
Experience in healthcare, medical products, supply chain, manufacturing, distribution, commercial operations, or corporate functions.
Ability to move fluidly between business discovery, solution design, coding, user testing, demonstration, and implementation planning.
Strong facilitation and storytelling skills; able to lead working sessions, demonstrate solutions, explain technical concepts simply, and drive decisions.
Comfort working in environments with imperfect data, evolving processes, and incomplete requirements.
Demonstrated ability to deliver results in fast-paced, collaborative, and execution-oriented organizations.
Equally comfortable engaging business stakeholders, building working solutions, and partnering with technical teams to productionize and scale them.

Responsibilities

Embed within a business domain and develop a deep understanding of its processes, workflows, systems, decisions, handoffs, and operational challenges.
Build trusted relationships with business leaders and frontline users; serve as a primary partner for AI-, data-, analytics-, and automation-enabled problem solving.
Continuously identify opportunities to improve decisions, productivity, customer experience, quality, cycle time, or business performance.
Shape and manage demand, focusing teams on opportunities with the highest potential value, feasibility, and readiness.
Challenge existing ways of working and help teams envision materially different workflows, not only incremental improvements.
Lead hands-on discovery sessions to define the business problem, user need, current workflow, constraints, baseline performance, and success measures.
Translate ambiguous business problems into clear solution hypotheses, experiments, and technical requirements.
Determine whether the best response is process improvement, workflow automation, RPA, analytics, machine learning, generative AI, or a combination of approaches.
Design AI-enabled workflows, agents, copilots, retrieval-augmented generation solutions, analytics and decision-support tools, workflow automations, and process automations.
Build working proofs of concept, prototypes, pilots, demonstrations, and minimum viable solutions using real data and approved enterprise platforms.
Test directly with users, incorporate feedback rapidly, and assess business value, usability, technical feasibility, controls, and scalability.
Make practical tradeoffs among speed, user value, technical complexity, and enterprise requirements.
Personally build functional solutions when the scope and technology are appropriate, including AI applications, agents, copilots, RAG solutions, analytics tools, workflow automations, RPA solutions, integrations, and decision-support tools.
Use Python, SQL, APIs, low-code tools, automation platforms, and AI-assisted coding capabilities to move quickly from an idea to a usable solution.
Work across enterprise data, APIs, business applications, AI platforms, analytics platforms, and workflow technologies to turn ideas into working outcomes.
Apply sound engineering practices for solution design, testing, documentation, security, reliability, maintainability, and secure data handling.
Define evaluation criteria with the business owner and incorporate testing, explainability, monitoring, controls, and human review as appropriate.
Troubleshoot technical and workflow issues during prototyping, piloting, implementation, and transition.
Ensure solutions align with enterprise architecture, cybersecurity, data governance, privacy, and Responsible AI requirements.
Maintain a bias for working solutions and measurable outcomes, not presentations or intermediate artifacts.
Determine which solutions can be owned and supported directly and which require transition to engineering, data science, platform, product, automation, or business technology teams.
Partner with technical teams to harden, productionize, integrate, deploy, scale, and support successful solutions.
Provide clear technical requirements, implementation guidance, documentation, and knowledge transfer when transitioning execution or long-term ownership.
Remain accountable for continuity of the solution and intended business outcome through handoff, deployment, adoption, and scale.
Drive user adoption, measure realized value against an agreed baseline, and refine solutions using user feedback and operational metrics.
Build and maintain a prioritized domain opportunity roadmap.
Create reusable components, prompts, agent patterns, automation templates, connectors, evaluation approaches, accelerators, and implementation patterns that help future teams deliver faster.