Lead ML/AI Engineer
New
J
JobgetherHealthcare technology
Based in the United StatesFull-TimeLead
Salary not disclosed
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Job Details
- 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 serving as the technical lead or architect of a production ML or decisioning system.
- Required Skills
- AWSPythonSQLMLFlowTerraformDatabricksMLOpsGenerative AI
Requirements
- Hold a bachelor's degree in Computer Science, Engineering, or a related field, or have equivalent professional experience.
- Have at least 8 years of experience in ML engineering, MLOps, or software engineering, including 4+ years operating machine learning systems in production.
- Have at least 2 years of experience serving as technical lead or architect of a production ML or decisioning system.
- Have built or operated prescriptive production systems such as recommendation, decisioning, prioritization, or experimentation platforms that allocate constrained resources against measurable outcomes.
- Have production experience with AWS data and machine learning services and infrastructure-as-code tools such as Terraform, CDK, or CloudFormation.
- Have production experience with Databricks, PySpark, Delta Lake, MLflow, or equivalent technologies.
- Have hands-on experience designing and operating event-driven or streaming systems using Kinesis, Kafka, or equivalent, including schema or contract management.
- Have strong Python and SQL skills and practical experience operating production ML workloads such as training pipelines, model serving, feature systems, or ML platform tooling.
- Have implemented CI/CD for ML workloads and monitored models and pipelines, including calibration across heterogeneous populations.
- Be able to establish engineering standards, improve team practices through code reviews, and mentor technical colleagues.
- Be willing and able to travel up to 10% for onsite meetings, team collaboration, and company events.
Responsibilities
- Own the decisioning and feedback layer connecting model scores to actions, experiments, outcomes, ROI calculations, and impact measurement.
- Lead implementation, auditability, and reproducibility of production savings calculations with medical economics and data science teams.
- Design real-time, event-driven integrations, including streaming pipelines, versioned schemas, data contracts, replay, event ordering, idempotency, and failure handling.
- Define integration contracts with partner teams for reliable data exchange and interoperability.
- Set compute, streaming, inference, and LLM budgets, and make cost a release criterion alongside latency and accuracy.
- Set production standards for models, CI/CD, registries, alerting, drift monitoring, coverage, and calibration across cohorts, markets, and payers.
- Own the target architecture and technical roadmap for AWS infrastructure and the Databricks lakehouse, including infrastructure as code, IAM, secure data handling, and containerized inference.
- Define production standards for generative AI applications, including LLM pipelines, agentic workflows, evaluation, retrieval, guardrails, and cost controls.
- Lead architecture and design reviews, document technical decisions, and mentor engineers and data scientists.
- Represent technical direction and recommendations to R&D leadership, finance, clinical, and operations stakeholders.
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