Staff Machine Learning Engineer System Integration 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: ELLKAY is seeking a staff-level machine learning engineer who can do more than build models. This role is for an engineer who can design, integrate, and operate AI-powered systems across products, data pipelines, APIs, enterprise applications, and workflow engines in production settings. The ideal candidate combines strong machine learning fundamentals with deep systems thinking. This person understands how to connect models, rules, messaging, data transformations, and operational software into reliable end-to-end solutions, especially in environments where interoperability, security, and uptime matter. This role sits at the intersection of machine learning engineering, backend engineering, systems integration, and platform architecture. The focus is not on isolated model experimentation alone, but on building production-grade AI workflows that move data across systems, trigger downstream actions, and create measurable business value. The scope includes workflow orchestration, API-driven integration, event-based processing, evaluation and observability, secure deployment, and reusable platform components that allow AI capabilities to operate inside larger enterprise ecosystems. Essential Duties & Responsibilities: • Design and implement AI-enabled workflows that span internal platforms, third-party systems, APIs, databases, and operational tools. • Build integration pipelines for ingestion, transformation, model inference, decisioning, and downstream actions in batch and real-time environments. • Architect reusable system components such as connectors, transformation layers, orchestration patterns, and event-driven services that make AI capabilities easier to deploy across the organization. • Develop production services and APIs that expose AI functionality safely and reliably to enterprise applications and users. • Partner with product, infrastructure, and data teams to operationalize ML and LLM capabilities in business-critical workflows. • Establish engineering standards for reliability, observability, versioning, testing, evaluation, and governance of AI systems in production. • Mentor engineers and help shape the technical roadmap for AI systems integration and platform architecture. Healthcare and Data Interoperability • Build systems that work with healthcare interoperability standards such as FHIR R4, HL7 v2, and USCDI data elements. • Integrate clinical terminologies and ontology services including LOINC, SNOMED, and RxNorm to support normalization, retrieval, and decision support workflows. • Design solutions that protect PHI and align with HIPAA and broader security requirements for regulated environments. Platform and Infrastructure • Build high-performance backend services using modern Python frameworks such as FastAPI and Pydantic, with strong error handling, retry logic, and asynchronous execution patterns. • Design cloud-native architectures using services such as AWS Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, ECS Fargate, Aurora PostgreSQL, DynamoDB, and S3. • Write production-grade infrastructure as code using AWS CDK in Python or TypeScript, including support for multi-tenant deployments and cross-account environments. • Implement secure and observable systems using structured logging, distributed tracing, metrics, alarms, encryption, secrets management, and least-privilege access controls. Evaluation and Quality • Design rigorous evaluation frameworks for ML and AI systems, including benchmark creation, held-out test sets, label-quality controls, leakage prevention, and model comparison methods. • Implement prompt versioning, controlled experiments, and A/B testing approaches to continuously improve model and workflow performance. • Drive strong engineering discipline in code quality, testing, deployment hygiene, and production support. Qualifications: • 10+ years of experience in software engineering, machine learning engineering, or closely related fields, including meaningful ownership of production systems. • Strong grounding in machine learning fundamentals, including model development, evaluation, deployment, and performance trade-offs. • Proven experience building distributed systems, integration platforms, or complex workflow-driven applications. • Expertise designing workflows across heterogeneous systems, including APIs, event streams, databases, and enterprise applications. • Strong programming ability in Python and working knowledge of Java or Go. • Experience with orchestration frameworks such as Airflow, Temporal, or Prefect. • Experience with messaging and streaming systems such as Kafka or Pub/Sub. • Experience designing and operating REST or gRPC APIs and data transformation pipelines. • Familiarity with modern AI application patterns, including LLMs, retrieval systems, vector databases, and model-integrated application workflows. • Strong communication skills and the ability to collaborate effectively across product, platform, security, and data teams. Preferred Qualifications • Experience building or extending integration engines or middleware platforms similar to Iguana, MuleSoft, or Apache Camel. • Experience in healthcare or another regulated industry with complex data movement, auditability, and compliance needs. • Experience with event-driven architectures and real-time processing systems at scale. • Familiarity with Amazon Bedrock, Claude family models, or comparable enterprise AI platforms. • Contributions to open-source software, interoperability initiatives, or internal developer platforms. • Degree in computer science, machine learning, or a related technical field. 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: $210,000-$230,000

Requirements

Must-haves

• Design and implement AI-enabled workflows that span internal platforms, third-party systems, APIs, databases, and operational tools.
• Build integration pipelines for ingestion, transformation, model inference, decisioning, and downstream actions in batch and real-time environments.
• Architect reusable system components such as connectors, transformation layers, orchestration patterns, and event-driven services that make AI capabilities easier to deploy across the organization.
• Develop production services and APIs that expose AI functionality safely and reliably to enterprise applications and users.
• Partner with product, infrastructure, and data teams to operationalize ML and LLM capabilities in business-critical workflows.
• Establish engineering standards for reliability, observability, versioning, testing, evaluation, and governance of AI systems in production.
• Mentor engineers and help shape the technical roadmap for AI systems integration and platform architecture.
Healthcare and Data Interoperability
• Build systems that work with healthcare interoperability standards such as FHIR R4, HL7 v2, and USCDI data elements.
• Integrate clinical terminologies and ontology services including LOINC, SNOMED, and RxNorm to support normalization, retrieval, and decision support workflows.
• Design solutions that protect PHI and align with HIPAA and broader security requirements for regulated environments.
Platform and Infrastructure
• Build high-performance backend services using modern Python frameworks such as FastAPI and Pydantic, with strong error handling, retry logic, and asynchronous execution patterns.
• Design cloud-native architectures using services such as AWS Lambda, API Gateway, Step Functions, EventBridge, SQS, SNS, ECS Fargate, Aurora PostgreSQL, DynamoDB, and S3.
• Write production-grade infrastructure as code using AWS CDK in Python or TypeScript, including support for multi-tenant deployments and cross-account environments.
• Implement secure and observable systems using structured logging, distributed tracing, metrics, alarms, encryption, secrets management, and least-privilege access controls.
Evaluation and Quality
• Design rigorous evaluation frameworks for ML and AI systems, including benchmark creation, held-out test sets, label-quality controls, leakage prevention, and model comparison methods.

Nice-to-haves

• Experience building or extending integration engines or middleware platforms similar to Iguana, MuleSoft, or Apache Camel.
• Experience in healthcare or another regulated industry with complex data movement, auditability, and compliance needs.
• Experience with event-driven architectures and real-time processing systems at scale.
• Familiarity with Amazon Bedrock, Claude family models, or comparable enterprise AI platforms.
• Contributions to open-source software, interoperability initiatives, or internal developer platforms.
• Degree in computer science, machine learning, or a related technical field.

Application prep

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Keywords to mirror

machine learningaiapimlllmpythonawstypescriptjavagorest

Platform: adp-workforce-now
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Parental Leave

Professional Development