Machine Learning Engineer

Machine Learning Engineers make machine learning and AI models reproducible, deployable, scalable, and supportable. They build the path from training data and experimentation to a versioned model service that can be released, monitored, retrained, and retired without guesswork.

The role centers on the model lifecycle and the platform beneath it. Machine Learning Engineers automate training and validation, manage features and model artifacts, optimize inference, implement machine learning operations (MLOps), and watch for changes in data, behavior, performance, reliability, and cost. They create the shared tooling that lets data scientists and application engineers move models into production safely.

• Reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts.
• Model-serving systems and APIs designed for appropriate latency, throughput, availability, scaling, and rollback.
• Feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls.
• Monitoring and alerting for data quality, drift, model performance, fairness, infrastructure health, latency, and cost.
• Reusable libraries, templates, environments, and delivery pipelines that give data scientists a tested path from experiment to production.

You are comfortable at the seam between modeling and software engineering. You can inspect a model, harden a pipeline, diagnose a production failure, and improve the platform so the same class of problem is easier to prevent next time.

You value repeatability over heroics. You work closely with data scientists on model behavior, data engineers on reliable inputs, AI Engineers on application integration, and platform and security teams on the environment in which the model runs.

• Strong programming and software-engineering practice, including testing, version control, packaging, automation, and production debugging.
• Working knowledge of model development, evaluation metrics, feature engineering, data splitting, tuning, and the limits of different modeling approaches.
• Experience with training and inference pipelines, containers, cloud or on-premises compute, artifact management, and automated deployment.
• Practical MLOps experience with model registries, lineage, reproducibility, monitoring, drift analysis, retraining, release controls, and rollback.
• The ability to balance model quality with reliability, interpretability, security, privacy, latency, throughput, and cost.

OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.

An opening may emphasize predictive models, computer vision, natural language models, ranking, anomaly detection, recommender systems, edge inference, generative AI model operations, or an enterprise ML platform. Some openings will focus more on model development, while others will focus more on serving and platform engineering.

Specific openings may name Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, graphics processing units, feature stores, model registries, experiment tracking, or infrastructure as code. OPEN Data Jobs will identify the required depth for each opening

Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening