AI/ML Data Platform Engineer (Senior) (Onsite)

Position Description: The Senior AI/ML Data Platform Engineer will be expected to design, build, and operationalize machine learning infrastructure and AI-driven data solutions on AWS. The successful candidate will bridge data engineering and MLOps, ensuring scalable, secure, and compliant AI/ML pipelines that support advanced analytics and decision-making across programs.

The AI/ML Data Platform Engineer creates and/or maintains operating systems, communications software, database packages, compilers, repositories, and utility and assembler programs. This position is responsible for modifying existing software and developing special-purpose software to ensure efficiency and integrity between systems and applications., * Design and implement end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and deployment on AWS (SageMaker, Glue, Lambda, Step Functions).

• Build and maintain MLOps infrastructure: model registries, CI/CD for ML models, experiment tracking (MLflow or SageMaker Experiments), and automated retraining pipelines.
• Develop scalable feature stores and data preprocessing pipelines using AWS Glue, EMR, or Spark on Databricks.
• Partner with data scientists and analysts to productionize models and translate research prototypes into robust, maintainable systems.
• Ensure ML systems meet government data security, privacy (PII/PHI handling), and compliance requirements (FISMA, NIST).
• Implement data versioning, lineage tracking, and model explainability/audit capabilities.
• Monitor deployed models for drift, performance degradation, and data quality issues; implement alerting and automated remediation.
• Contribute to data platform architecture decisions, including lakehouse design, data cataloging (AWS Glue Data Catalog / Apache Atlas), and access controls.
• Document architectures, pipeline designs, and operational runbooks to government documentation standards.

Education: This position requires a Bachelor’s degree from an accredited college or university with a major in computer science, information systems, engineering, business, or other related scientific or technical discipline. Three (3) years of equivalent experience in a related field may be substituted for the Bachelor’s degree. (Note: A Master’s degree is preferred.)

General Experience: The proposed candidate must have twelve (12) years of computer experience in information systems design. Other experience required:

• 6+ years of data engineering or software engineering experience, with 2+ years focused on ML platform or MLOps engineering.
• Deep expertise with AWS ML/AI services: SageMaker, Glue, EMR, Lambda, Step Functions, Kinesis, S3.
• Strong Python skills; experience with ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost).
• Experience building and maintaining MLOps pipelines and CI/CD for ML workflows.
• Solid understanding of distributed computing (Spark/EMR) and large-scale data processing.
• Familiarity with government data security requirements and experience operating in FedRAMP-authorized AWS environments.
• Experience with infrastructure-as-code tools (Terraform, AWS CDK, CloudFormation).

Specialized Experience: The proposed candidate must have at least ten (10) years of experience in IT systems analysis and programming including the following:

• Experience with generative AI / LLM integration (AWS Bedrock, LangChain) in enterprise or government contexts.
• Knowledge of responsible AI, model governance, and bias detection frameworks.
• Experience with real-time/streaming ML pipelines using Kinesis or Kafka.
• Familiarity with data mesh or federated data architectures. xrbllit
• Background in healthcare, public health, or social services data (HIPAA/42 CFR Part 2 awareness).

Preferred Certifications

• AWS Certified Machine Learning - Specialty
• AWS Certified Data Analytics - Specialty
• AWS Certified Solutions Architect - Associate or Professional
• Databricks Certified Associate Developer for Apache Spark