Lead ML/AI Engineer

Jobgether; United States; À distance; USD 140 000 - 190 000; Plein temps

Avantages offerts par ce poste

Remote-first work environment

Hybrid option in Nashville

PTO and holidays

Parental leave

Health, dental and vision insurance

Life and disability insurance

HSA and 401(k) with employer match

Résumé du poste

Jobgether in the United States is seeking a senior ML/AI engineering leader to own the production platform that turns model predictions into actions across healthcare markets. You will work with AWS, Databricks lakehouse, and PHI controls, establishing standards for decisioning, streaming, MLOps, and generative AI while remaining hands-on with critical components.

This role requires deep software and infrastructure expertise, strong leadership, and a track record of operating production ML

Qualifications

• 8+ years in ML engineering, MLOps or software engineering.
• 4+ years operating ML systems in production.
• 2+ years as technical lead or architect of production ML/decisioning systems.
• Experience with AWS data/ML services and IaC tools (Terraform, CDK, CloudFormation).
• Experience with Databricks, PySpark, Delta Lake, MLflow.
• Hands-on with event-driven/streaming systems (Kinesis, Kafka).
• Strong Python and SQL for production ML workloads.
• CI/CD for ML workloads and monitoring.
• Willingness to travel up to 10% for onsite meetings and events.

Responsabilités

• Own the decisioning and feedback layer connecting model scores to actions, including ranking and ROI calculations.
• Lead production savings calculations with medical economics and data science.
• Design real-time integrations between model outputs and operational systems.
• Define and maintain integration contracts with partner teams for data exchange.
• Establish cost-to-serve standards for compute, streaming, and LLM budgets.
• Set production standards for ML models, CI/CD, monitoring, and drift calibration.
• Build/model promotion gates to ensure deployment readiness.
• Own architecture for AWS-native infra and Databricks lakehouse with IaC and secure data handling.
• Define direction for generative AI apps with guardrails and cost controls.
• Collaborate with data scientists, medical economics, and engineering on shared pipelines.
• Establish engineering standards and enforce code reviews and release discipline.
• Lead architecture reviews and mentor engineers and data scientists.
• Represent technical direction to R&D, finance, clinical and operations stakeholders.

Connaissances

ML engineering

MLOps

Python

SQL

Leadership

CI/CD for ML

Mentoring

Formation

Bachelor's degree in Computer Science, Engineering, or related field

Outils

Terraform

CDK

CloudFormation

Databricks

PySpark

Delta Lake

MLflow

Kinesis

Kafka

Docker

Description du poste

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

Responsibilities

• 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, containerised 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 modelling 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

The ideal candidate is an experienced ML/AI engineering leader who has built and operated production systems that influence real‑world decisions and measure their impact. You should combine strong software and infrastructure expertise with technical leadership, sound engineering judgement, and the ability to collaborate across multidisciplinary teams.

Required Qualifications

• 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, prioritisation, 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 modelling.
• Familiarity with healthcare data, including claims, pharmacy, eligibility, prior authority, and electronic health records (EHR), as well as PHI handling requirements.
• Experience with uplift modelling, bandit algorithms, sequential decisioning, or holdout‑based measurement in production.
• Experience with containerised 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.