Lead ML/AI Engineer

Jobgether; Oregon; Híbrido; USD 190.000 - 240.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Remote-first work environment

Paid time off and holidays

Parental leave

Health, dental, vision insurance

Descripción de la vacante

Jobgether in the United States seeks a Lead ML/AI Engineer to own production infrastructure turning model predictions into measurable actions across healthcare markets. You will manage a real-time decision platform, connecting models, care teams, payer systems, and feedback loops, using AWS-native services with a Databricks lakehouse under PHI controls.

You will define standards for decisioning, streaming, MLOps, and generative AI while staying hands-on with core components.

Formación

• 8+ years in ML engineering, MLOps, or software engineering with 4+ years in production.
• 2+ years as technical lead or architect of production ML/decisioning systems.
• Experience building prescriptive production systems allocating resources for measurable outcomes.
• Production experience with AWS data/ML services and infra-as-code tools like Terraform/CDK/CloudFormation.
• Databricks, PySpark, Delta Lake, MLflow or equivalent.
• Hands-on with event-driven/streaming systems (Kinesis, Kafka), including schema management.
• Strong Python and SQL in production ML workloads and tooling.
• CI/CD for ML and monitoring/calibration across populations.
• Mentorship and establishment of engineering standards.

Responsabilidades

• Own the decisioning and feedback layer linking model scores to actions and ROI calculations.
• Lead production savings calculations with medical economics and data science teams.
• Design real-time integrations between model outputs and operations, including streaming pipelines and data contracts.
• Define and maintain integration contracts for reliable data exchange.
• Establish cost-to-serve standards and budgets at model/decision levels.
• Set production standards for CI/CD, model registries, alerting, drift monitoring, and calibration.
• Build and maintain model promotion gates for production readiness.
• Own target architecture and roadmap for AWS-native infrastructure and Databricks lakehouse, with IaC and secure PHI handling.
• Define direction for generative AI, LLM pipelines, guardrails, and cost controls.
• Collaborate with data scientists, medical economics, and engineering on shared interfaces.
• Establish engineering standards for reviews, testing, reproducibility, and release discipline.
• Lead architecture/design reviews and mentor engineers and data scientists.
• Represent technical direction to R&D, finance, clinical, and operations stakeholders.

Conocimientos

ML engineering

MLOps

Python

SQL

CI/CD for ML

Data pipelines

Model serving

Stakeholder communication

Educación

Bachelor's degree in CS or related

Herramientas

AWS

Databricks

Terraform

CloudFormation

Kinesis/Kafka

MLflow

Descripción del empleo

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead ML/AI Engineer based in the United States.Take a leading role in building the production infrastructure that turns machine learning predictions into measurable actions and outcomes across healthcare markets. You will own the engineering behind a real-time decision platform, connecting models, care teams, payer systems, and feedback loops. Combining AWS-native services with a Databricks lakehouse, you will establish scalable, secure, and cost-efficient systems that operate under strict protected health information (PHI) controls. You will define technical standards for decisioning, streaming, MLOps, and generative AI applications while remaining hands‑on with critical components. Working closely with data science, engineering, medical economics, clinical, and operations teams, you will ensure that models are reliable, measurable, and ready for production. This is a senior individual contributor position with significant technical ownership and influence over the platform’s long‑term architecture.

Accountabilities

• Own the decisioning and feedback layer connecting model scores to actions, including action selection and ranking, holdout and experiment assignment, outcome capture, production ROI calculations, and pipelines measuring the impact of actions and channels.
• Lead the engineering implementation, auditability, and reproducibility of production savings calculations in collaboration with medical economics and data science teams.
• Design event‑driven, real‑time integrations between model outputs and operational systems, including streaming pipelines, versioned schemas, data contracts, replay mechanisms, event ordering, idempotency, and failure handling.
• Define and maintain integration contracts with partner teams to ensure reliable data exchange and system interoperability.
• Establish cost‑to‑serve standards by defining compute, streaming, inference, and LLM spending budgets at the model and decision levels, making cost a release criterion alongside latency and accuracy.
• Set production standards for ML models, including CI/CD, model registries, alerting, drift monitoring, coverage, and calibration across different cohorts, markets, and payers.
• Build and maintain model promotion gates to ensure that models and ranking systems meet production requirements before deployment.
• Own the target architecture and technical roadmap for the AWS‑native infrastructure and Databricks lakehouse, including infrastructure as code, IAM, secure data handling, containerized inference, and managed foundation‑model services under PHI controls.
• Define technical direction and production standards for generative AI applications, including LLM pipelines and agentic workflows, with appropriate evaluation, retrieval, guardrails, and cost controls.
• Collaborate with data scientists on modeling and measurement approaches, medical economics teams on savings methodology, and engineering teams on shared pipelines, interfaces, and contracts.
• Establish engineering standards covering code reviews, automated testing, reproducibility, documentation, and release discipline.
• Lead architecture and design reviews, document technical decisions, and mentor engineers and data scientists on system design and production engineering practices.
• Represent the team's technical direction and recommendations to R&D leadership, finance, clinical, and operations stakeholders.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.
• At least 8 years of experience in ML engineering, MLOps, or software engineering, including 4+ years operating machine learning systems in production.
• At least 2 years of experience serving as the technical lead or architect of a production ML or decisioning system.
• Demonstrated experience building or operating prescriptive production systems, such as recommendation, decisioning, prioritization, or experimentation platforms that allocate constrained resources against measurable outcomes.
• Production experience with AWS data and machine learning services, as well as infrastructure‑as‑code tools such as Terraform, CDK, or CloudFormation.
• Production experience with Databricks, PySpark, Delta Lake, MLflow, or equivalent technologies.
• Hands‑on expertise designing and operating event‑driven or streaming systems using technologies such as Kinesis, Kafka, or equivalent, including schema or contract management.
• Strong Python and SQL skills, with practical experience operating production ML workloads such as training pipelines, model serving, feature systems, or ML platform tooling.
• Experience implementing CI/CD for ML workloads and monitoring models and pipelines, including calibration across heterogeneous populations.
• Proven ability to establish engineering standards, improve team practices through code reviews, and mentor technical colleagues.
• Willingness and ability to travel up to 10% for onsite meetings, team collaboration, and company events.

Preferred Qualifications

• Experience building and evaluating machine learning models using scikit-learn, XGBoost, or PyTorch.
• Exposure to healthcare economics or value‑based care, including savings methodologies, attribution, and payer contract constraints.
• Experience with dbt and lakehouse data modeling.
• Familiarity with healthcare data, including claims, pharmacy, eligibility, prior authorization, and electronic health records (EHR), as well as PHI handling requirements.
• Experience with uplift modeling, bandit algorithms, sequential decisioning, or holdout‑based measurement in production.
• Experience with containerized model serving and staged or canary deployment strategies.
• Familiarity with Amazon Bedrock or other managed foundation‑model platforms operating under a HIPAA Business Associate Agreement (BAA).
• Experience with agentic frameworks and production retrieval systems.
• Demonstrated experience building or operating LLM‑based applications, including evaluation frameworks and guardrails.

Core Competencies

• Engineering Mentality: Identifies common technical challenges and builds reusable platforms that make future model deployments faster and safer.
• Scientific Approach: Treats system designs as hypotheses, instruments performance, and adapts based on measurable evidence.
• Simplicity: Avoids unnecessary complexity and creates maintainable systems for future users and engineers.
• Ownership: Takes systems from design through deployment and monitoring, remaining accountable for their real‑world performance and outcomes.
• Collaboration: Works effectively across data science, data engineering, software engineering, medical economics, clinical, and operations teams to establish clear interfaces and shared standards.
• Communication: Explains technical systems, costs, and business impact in clear terms that clinical and financial stakeholders can use.

Benefits

• Remote‑first work environment, with the option of a hybrid arrangement in Nashville.
• Generous paid time off (PTO) and holiday plans.
• Paid parental leave and support for work‑life balance, flexibility, and autonomy.
• Medical, dental, and vision insurance starting on Day 1.
• Life and disability insurance.
• Wellness resources and an employer HSA contribution.
• 401(k) retirement plan with employer matching contributions.
• Opportunity to contribute to meaningful healthcare initiatives and improve patient outcomes through technology.
• Inclusive and collaborative working environment that encourages honest feedback, diverse perspectives, and authenticity.
• Commitment to equitable compensation and employee well‑being.