Senior AI Data Platform Engineer

3 Reasons Consulting•San Diego, CA

6d•Onsite

About The Position

3 Reasons Consulting (3RC) is a growing government consulting and technology services firm supporting federal and Department of Defense customers with mission-critical technology, artificial intelligence, data engineering, cybersecurity, and modernization initiatives. 3RC is seeking a Senior AI Data Platform Engineer to support the Naval Health Research Center (NHRC) in San Diego, CA. This position will help design, build, integrate, and maintain the data platforms and infrastructure required to support advanced AI, machine learning, analytics, and research applications. The Senior AI Data Platform Engineer will serve as a senior technical resource responsible for building and optimizing data platforms that enable AI/ML development, experimentation, and operational deployment. The ideal candidate combines strong data engineering and platform engineering expertise with an understanding of AI/ML workloads. This role will work across data engineering, cloud infrastructure, DevSecOps, cybersecurity, and AI/ML teams to create secure, scalable, automated, and reliable data environments.

Requirements

Security+ or other DoD 8570/8140-compliant certification.
Active Secret clearance as required by the contract.
3+ years of experience in data engineering, data platform engineering, cloud engineering, or a related technical discipline.
Strong experience designing and implementing enterprise data platforms and pipelines.
Advanced experience with SQL and relational databases.
Experience developing ETL/ELT pipelines and data-processing workflows.
Experience with cloud, on-premises, or hybrid infrastructure.
Experience with CI/CD and DevSecOps practices.
Strong understanding of data architecture, data modeling, data quality, and data security.
Demonstrated ability to troubleshoot complex data and infrastructure problems.
Excellent technical documentation and communication skills.

Nice To Haves

Bachelor's degree in Computer Science, Data Engineering, Computer Engineering, Information Technology, or related technical discipline.
Experience supporting Department of Defense or Navy programs.
Experience building data platforms specifically supporting AI/ML or advanced analytics.
Experience with Apache Spark, Databricks, Advana, Kafka, Airflow, or similar data-engineering platforms.
Experience with data lake and lakehouse architectures.
Experience with MLflow, Kubeflow, Feast, or other MLOps technologies.
Experience supporting generative AI, LLM, RAG, vector databases, embeddings, or AI agents.
Experience with PostgreSQL, MongoDB, Elasticsearch/OpenSearch, Redis, or vector databases.
Experience with Docker, Kubernetes, Helm, Terraform, Ansible, GitLab, Jenkins, or Argo CD.
Experience implementing data governance, cataloging, lineage, and metadata-management solutions.
Experience with GPU-enabled AI infrastructure and high-performance computing environments.
Familiarity with NIST 800-53, RMF, DISA STIGs, DoD 8140/8570, and related cybersecurity requirements.
Security+ or other DoD-compliant cybersecurity certification.

Responsibilities

Design, build, configure, and maintain enterprise data platforms supporting AI/ML and advanced analytics.
Develop scalable architectures for ingesting, processing, storing, and serving large and complex datasets.
Integrate structured, unstructured, and semi-structured data sources into AI-ready environments.
Develop data pipelines supporting model training, validation, inference, and operational analytics.
Optimize data platforms for performance, scalability, availability, and cost efficiency.
Support the integration of data platforms with AI/ML applications, APIs, analytics tools, and enterprise systems.
Develop and maintain reliable ETL/ELT pipelines for high-volume and diverse datasets.
Build automated workflows for data ingestion, transformation, validation, enrichment, and delivery.
Implement data quality, validation, lineage, and governance processes.
Develop reusable data-processing frameworks and services.
Troubleshoot data pipeline failures, performance issues, and integration problems.
Support batch and real-time/streaming data processing requirements.
Build data infrastructure supporting machine learning model development and MLOps.
Prepare and optimize datasets for AI/ML training and inference workloads.
Support feature engineering, feature stores, model datasets, and model-serving infrastructure.
Integrate data platforms with ML frameworks and MLOps tools.
Support LLM, generative AI, RAG, vector search, and embedding-based applications where applicable.
Design data architectures that support reproducibility, traceability, and model lifecycle management.
Deploy and maintain data workloads across cloud, on-premises, and hybrid environments.
Support containerized data applications using Docker and Kubernetes.
Develop Infrastructure as Code using tools such as Terraform or Ansible.
Integrate data platforms into automated CI/CD and DevSecOps pipelines.
Support platform monitoring, logging, alerting, capacity management, and performance optimization.
Automate routine platform and data-engineering activities.
Design, configure, and optimize relational and NoSQL database environments.
Work with technologies such as PostgreSQL, SQL Server, MongoDB, Elasticsearch/OpenSearch, or comparable platforms.
Support data lakes, data warehouses, object storage, and distributed data-processing environments.
Develop efficient SQL queries and optimize database performance.
Evaluate emerging data technologies and recommend solutions based on mission and technical requirements.
Implement security controls throughout the data platform architecture and development lifecycle.
Support Risk Management Framework (RMF), NIST 800-53, DISA STIG, and DoD cybersecurity requirements.
Implement appropriate access controls, encryption, authentication, auditing, and data-protection mechanisms.
Support vulnerability management, security assessments, and remediation activities.
Maintain documentation supporting security authorization and operational requirements.
Collaborate with cybersecurity teams, ISSOs, ISSMs, system administrators, and application teams.
Serve as a senior technical advisor for data platform architecture and engineering decisions.
Provide technical guidance and mentorship to data engineers and platform engineers.
Participate in architecture reviews, technical design sessions, and engineering working groups.
Translate mission requirements into scalable and supportable technical solutions.
Develop technical documentation, architecture diagrams, data-flow diagrams, and operational procedures.