Sr. Engineer AI

Medline; Northbrook (IL); On-site; USD 140,000 - 190,000; Full time

Job summary

Medline seeks a Senior AI Engineer who is a hands-on AI, data, analytics, and automation practitioner embedded in business domains such as Commercial, Operations, or Product. You will identify opportunities, prototype pilots, and deliver practical solutions that drive measurable value.

You will work directly with business teams to translate workflows into proofs of concept, demonstrations, and production-ready automations using Python, SQL, APIs, and low-code tools.

Qualifications

• Experience delivering AI/automation solutions in a business context.
• Hands-on prototyping, piloting, and productionization of solutions.
• Ability to translate business problems into technical requirements.

Responsibilities

• Embed within a business domain and understand processes, workflows, and challenges.
• Build trusted relationships with business leaders and frontline users for AI-driven problem solving.
• Lead hands-on discovery, define problems, baselines, and success measures.
• Design AI-enabled workflows, agents, copilots, and analytics tools.
• Build working proofs of concept and pilots using real data on approved platforms.
• Test with users, iterate quickly, and assess value, feasibility, and risk.
• Ensure alignment with enterprise architecture, security, and Responsible AI.
• Scale and transition solutions to engineering or platform teams as needed.

Skills

AI development

Data analytics

Automation

Python

SQL

APIs

Low-code

AI-assisted coding

Job description

Job Summary

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.

Job Description

Trusted Partnership & Opportunity Identification

• 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.

Solution Design & Rapid Prototyping

• 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.

Hands-On AI, Analytics & Automation Delivery

• 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.

Scale, Transition & Business Impact

• 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