Staff ML Engineer AI Platform & Agents at ELLKAY

Job description

ELLKAY started out providing connectivity solutions to laboratories and within a few years, grew to also provide data management solutions to ambulatory organizations. ELLKAY is now a trusted data management partner in five healthcare segments. ELLKAY’s solutions continue to serve laboratories and ambulatory practices and have expanded to empower hospitals and health systems, healthcare IT vendors, ambulatory practices, health plans, and other healthcare organizations with cutting-edge technologies and solutions that drive their growth and interoperability strategies. Today, ELLKAY remains true to our core values, building strong partner relationships and offering unparalleled service and support while providing innovative, scalable solutions to the challenges our customers face in today’s data-rich world. ELLKAY's experience, customer-focused approach, and reputation for innovation, speed, and accuracy differentiate ELLKAY as a premier partner for your interoperability needs and data management strategy. Job Description: Lead the design and delivery of shared AI/ML services for ELLKAY's migration, archive, connector, and interoperability products. This is a hands-on technical leadership role covering model development, LLM applications, governed agents, reusable serving interfaces, and engineering standards. The engineer will mentor the team and partner with Data Platform, product, clinical, and security leaders to turn pilots into measurable production capabilities. Essential Duties & Responsibilities • Define the shared AI architecture, model-routing approach, retrieval interfaces, tool contracts, and release criteria. Lead design reviews and mentor the distributed ML team. • Design governed agent experiences for implementation guidance, migration recommendations, operational explanations, and archive retrieval. • Integrate with the shared agent gateway and existing platform APIs. Enforce tenant and purpose checks, approvals for actions, async job status, and auditable tool execution. • Choose between rules, classical ML, retrieval, and LLMs using measured quality, latency, and cost. Coordinate rollout from synthetic sandbox to approved customer pilots. • Develop reference implementations and own difficult production failures, including model regressions, retrieval errors, invalid tool actions, and latency/cost overruns.Qualifications and expected outcomes Qualifications: • 8+ years of relevant software/ML engineering experience, with ownership of production ML systems and technical leadership across teams. • Strong Python and SQL, sound software design, automated testing, API development, and experience with PyTorch, scikit-learn, or comparable ML frameworks. • Hands-on model training, feature engineering, model selection, error analysis, statistical evaluation, and techniques to prevent dataset leakage. • Production experience with LLM applications, retrieval-augmented generation, embeddings, tool calling, model routing, and structured outputs. • Ability to design distributed services, cloud serving, access controls, model/prompt lifecycle management, and cost/performance tradeoffs. • Evidence of mentoring engineers and translating product needs into deliverable technical designs. A relevant degree or equivalent practical experience is acceptable. Preferred qualifications Healthcare integration or regulated enterprise AI; MCP/tool-calling experience; AWS and Snowflake; familiarity with HL7, FHIR, and migration or archive workflows. Framework experience such as LangGraph or an equivalent orchestration approach is useful. Expected outcomes • First 90 days: agree the AI architecture and ownership boundaries, establish evaluation/release standards, and deliver a reference implementation for the first two use cases. • By six months: deliver a common AI service consumed by two workflows, mentor the four specialists, and complete a synthetic or no-PHI agent pilot with measured task quality, latency, and unit cost. • By 12 months: demonstrate common-service adoption by at least two product families, a measured improvement in an agreed customer workflow, and reproducible evaluation evidence for 100% of production AI releases. • Quality, performance, and business-impact targets will be calibrated to the selected use cases and their initial baselines. Delivery depends on the corresponding platform capabilities and approved customer pilot scope. Benefits: ELLKAY offers a comprehensive and competitive benefit package that starts day one! Including: • Medical, Dental, and Vision benefits • Employer-paid Life and LTD • 401k w/ matching – once eligibility is met • Work/life balance • Paid Volunteer Program • Flexible working hours • Generous FTO • Remote work options • Employee Discounts • Parental Leave • Gym membership / Exercise class stipends Our awesome culture includes: • Working with talented, collaborative, and friendly people who love what they do • Professional growth within • Innovation environment • On site in HQ Free daily lunches Awards: • 2025 Top Workplaces Employee Appreciation • 2025 Top Workplaces Employee Well-Being • 2025 Top Workplaces Professional Development • 2025 Top Workplaces Leadership • 2026 USA Today Top Workplaces • 2026 NJ Top Workplaces Additional information At ELLKAY, we are committed to operating as a hybrid workplace, offering employees flexibility in how they structure their time between in-office and remote work. We recognize the significance of fostering connections, collaboration, and creativity within our office culture and its positive impact on our business. Our philosophy of operating as a hybrid workplace underscores our dedication to enabling employees to tailor work-life balance to their individual preferences. For those who do not live within 40 miles of one of our offices, we are open to considering remote work for candidates whose skills and experience strongly align with the role. While we prioritize a hybrid work environment for most roles, we understand the importance of flexibility and are open to remote work for specific positions and specialized skill sets. For more information on our company, visit www.ELLKAY.com. ELLKAY LLC is a Smoke-Free Workplace. ELLKAY, LLC provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Compensation Range: $180,000 - $220,000

Requirements

Must-haves

Essential Duties & Responsibilities
Define the shared AI architecture, model-routing approach, retrieval interfaces, tool contracts, and release criteria. Lead design reviews and mentor the distributed ML team.
Design governed agent experiences for implementation guidance, migration recommendations, operational explanations, and archive retrieval.
Integrate with the shared agent gateway and existing platform APIs. Enforce tenant and purpose checks, approvals for actions, async job status, and auditable tool execution.
Choose between rules, classical ML, retrieval, and LLMs using measured quality, latency, and cost. Coordinate rollout from synthetic sandbox to approved customer pilots.
Develop reference implementations and own difficult production failures, including model regressions, retrieval errors, invalid tool actions, and latency/cost overruns.Qualifications and expected outcomes
8+ years of relevant software/ML engineering experience, with ownership of production ML systems and technical leadership across teams.
Strong Python and SQL, sound software design, automated testing, API development, and experience with PyTorch, scikit-learn, or comparable ML frameworks.
Hands-on model training, feature engineering, model selection, error analysis, statistical evaluation, and techniques to prevent dataset leakage.
Production experience with LLM applications, retrieval-augmented generation, embeddings, tool calling, model routing, and structured outputs.
Ability to design distributed services, cloud serving, access controls, model/prompt lifecycle management, and cost/performance tradeoffs.
Evidence of mentoring engineers and translating product needs into deliverable technical designs. A relevant degree or equivalent practical experience is acceptable.
Employer-paid Life and LTD
Work/life balance
Paid Volunteer Program
Flexible working hours
Generous FTO
Remote work options
Employee Discounts

Nice-to-haves

Expected outcomes
First 90 days: agree the AI architecture and ownership boundaries, establish evaluation/release standards, and deliver a reference implementation for the first two use cases.
By six months: deliver a common AI service consumed by two workflows, mentor the four specialists, and complete a synthetic or no-PHI agent pilot with measured task quality, latency, and unit cost.
By 12 months: demonstrate common-service adoption by at least two product families, a measured improvement in an agreed customer workflow, and reproducible evaluation evidence for 100% of production AI releases.
Quality, performance, and business-impact targets will be calibrated to the selected use cases and their initial baselines. Delivery depends on the corresponding platform capabilities and approved customer pilot scope.

Application prep

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Platform: adp-workforce-now
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Parental Leave

Professional Development