Role Description Our client is looking for an AI / Data Engineer to design and deliver data platforms, pipelines and AI-enabled solutions for our clients. This is a hands-on consulting and delivery role for someone who can take ownership of ambiguous client problems, shape a practical technical approach, and deliver robust solutions across data engineering, data architecture and AI innovation. You will work with the company’s colleagues, client stakeholders and external technical teams to acquire, structure, transform and expose data through analytics, applications, APIs and AI-enabled experiences. Duties & Responsibilities - Own the delivery of client data and AI engineering work from discovery through design, implementation, testing and deployment. - Work with client stakeholders to understand business problems, clarify requirements and translate them into practical technical solutions. - Design and build data ingestion pipelines from APIs, third-party systems, files, online sources and operational platforms. - Develop scalable warehouse and transformation layers that convert raw data into trusted, reusable client data products. - Apply sound data architecture, modelling, quality, lineage and governance practices. - Identify and implement opportunities to use AI, automation, retrieval and agent-based workflows within client solutions. - Build or support APIs, data access layers and application integrations that make data products usable by reporting tools, software applications and AI experiences. - Contribute reusable technical patterns, documentation and engineering standards across the company. Qualifications - Ownership and Client Problem-Solving: You take ownership of outcomes rather than waiting for fully defined requirements. You are comfortable working through ambiguity, asking the right questions, identifying gaps and helping clients move from a business problem to a practical solution. - AI-First Mindset: You are interested in how AI can improve data collection, enrichment, research, automation, retrieval and decision support. You do not need to be an AI researcher, but you should be comfortable evaluating where LLMs, agents and AI-enabled workflows can create practical value. - Data Engineering and Warehousing Capability: You have strong hands-on experience designing and delivering modern data pipelines and warehouse solutions. You understand ingestion patterns, orchestration, data modelling, transformation layers, quality controls, observability, lineage, performance and security. - Collaborative Delivery Mindset: You work well in a consulting environment, communicate clearly with technical and non-technical stakeholders, and are comfortable sharing ideas, challenging assumptions and working closely with client and internal delivery teams. Requirements - Approximately five to ten years of relevant experience in data engineering, analytics engineering, data platform delivery or related technical consulting roles. - Practical experience with several of the following: - Modern cloud data warehouses such as Snowflake, BigQuery, Redshift, Databricks or Synapse. - SQL, data modelling and transformation frameworks such as dbt. - Cloud platforms, particularly AWS, Azure or GCP. - API integration, external data ingestion and pipeline orchestration. - Data quality, monitoring, observability and governance practices. - Software engineering practices including Git, automated testing and CI/CD. - Building data products for analytics, reporting, APIs or software applications. - Experience with AI orchestration frameworks, LLM tool calling, retrieval workflows, vector search, agent-based systems or related AI technologies. Success in This Role Success will mean delivering reliable, scalable and commercially useful data and AI solutions for the clients. You will combine sound engineering discipline with strong client problem-solving: understanding the real need, designing the right solution, delivering it effectively and helping clients derive measurable value from their data. Kindly regard your application as unsuccessful if you have not heard from the agency within 2 weeks.

• Partner with the CTO and leadership to set the Intelligence strategy and roadmap; own the execution. • Build, hire, and develop the Intelligence team — set the bar for craft, shape the operating cadence, and build the collaboration patterns with product, platform, and engineering. • Stand up the canonical data substrate: schema discipline, tenancy isolation, data contracts, lineage, and governance that AI/ML workloads run cleanly against. • Stand up the ML and AI platform: model lifecycle, feature store, vector store, training and serving infrastructure, and MLOps practice. • Lead the learning and reasoning capabilities of the platform: RAG architectures, agentic data systems, knowledge graphs, and the patterns that let Stratus's data compound into platform intelligence. • Develop and drive evaluation frameworks measuring model quality, agent reliability, drift, and platform effectiveness — make AI workloads observable to engineering, product, and customer success. • Drive the build-vs-buy posture for the AI/ML stack; set production readiness standards for AI workloads in close collaboration with the platform team. • Partner with product on the AI use case portfolio; engage directly with customers when needed to ground Intelligence decisions in real workflow problems.

Role Description We are looking for a Data Scientist / ML Platform Engineer to contribute across the full ML development lifecycle — from model building and experimentation to production deployment and monitoring. Core responsibilities are in applied data science and MLOps, with secondary contributions to data engineering and light platform operations. This role works within established platform patterns alongside dedicated infrastructure engineers, without requiring their involvement for routine ML and data tasks. All work is performed in a HIPAA-governed, FedRAMP-compliant healthcare analytics environment. - Develop, train, and evaluate ML models (classification, regression, clustering, anomaly detection) and contribute to LLM-based capabilities such as RAG pipelines and prompt evaluation. - Support model governance and deployment practices using MLFlow, including experiment tracking, model versioning, registry promotion workflows, and automated testing across the ML lifecycle. - Contribute to production ML operations: model performance monitoring, drift detection, automated alerting, and incident escalation to maintain reliability and SLA compliance. - Build and improve model serving infrastructure, feature pipelines, and lifecycle automation to support reproducible, scalable model development and inference. - Apply explainability techniques (e.g., SHAP, LIME) and produce technical documentation to support stakeholder transparency and compliance requirements. - Contribute to data ingestion, ELT/ETL transformation, and pipeline reliability using Spark and SQL-based frameworks within Snowflake and Databricks environments. - Support pipeline orchestration, medallion architecture conventions, and data stewardship practices (metadata management, PII handling, lineage tracking in Unity Catalog). - Perform occasional system administration tasks in collaboration with platform teams, including environment configuration, access management, compute troubleshooting, and secrets handling using platform-native tools. Qualifications - Associate's with 6 years, or Bachelor's degree with 4+ years of relevant experience, or Master's degree with 2+ years of relevant experience or High School diploma with 8 years of experience in lieu of a degree. - Demonstrated experience with SQL and Python, including Python-based ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, or TensorFlow). - Hands-on experience with MLFlow or equivalent tools for experiment tracking, model governance, and lifecycle management. - Strong understanding of SDLC fundamentals and experience with GitHub or equivalent version control. - Experience with distributed compute environments (e.g., Spark, Databricks) and cloud-native services. - Basic proficiency with Bash or shell scripting for automation and environment setup. - Ability to collaborate across multidisciplinary teams and communicate technical concepts to varied audiences. - Ability to obtain and maintain a Public Trust clearance. - US citizenship required or Green Card holder and must have been in the USA for 3 of the last 5 years. Requirements - Experience with MLOps practices including CI/CD for ML, containerization, feature pipeline automation, and model deployment frameworks. - Experience with Databricks E2 components (Unity Catalog, Feature Store, Delta Live Tables) and/or model serving and drift monitoring tools (e.g., Databricks Model Serving, Evidenly, etc.). - Experience with LLM frameworks (e.g., LangChain, LlamaIndex, Hugging Face Transformers) and familiarity with model explainability libraries (e.g., SHAP, LIME). - Advanced Spark performance optimization experience and/or API development using Databricks REST APIs. - Experience with healthcare analytics data (preferably Medicare or Medicaid) and familiarity with HIPAA or FedRAMP compliance constraints. - Experience building data pipelines in a Snowflake or Databricks environment. - Familiarity with orchestration tools (Airflow, Databricks Workflows). - Exposure to streaming data patterns using Spark Structured Streaming, Delta Live Tables, or Kafka. - Familiarity with environment reproducibility tooling (Docker, conda) and scripting (Python, Bash) to support automation and CI/CD tasks. Benefits - Target Salary Range: $80,000 - $128,000. This represents the typical salary range for this position. - Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. - Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Company Description Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can’t be done by solving the most daunting challenges facing our customers.

• Design, build, and evolve Data Warehouses, Data Marts, and enterprise data models to support analytics and business decision-making • Design, implement, and optimize scalable ELT pipelines and data processing solutions, ensuring reliability, performance, and maintainability • Drive technical decisions related to data architecture, platform evolution, data modeling approaches, and integration patterns • Establish and maintain data governance practices, metadata standards, data lineage, security controls, and access management policies • Ensure data quality, observability, monitoring, and operational excellence across the data platform • Evaluate and introduce technologies, tools, and best practices that improve platform scalability, efficiency, and maintainability • Support and enhance self-service analytics capabilities through Apache Superset and other BI solutions • Collaborate closely with BI Engineers, Business Analysts, and business stakeholders to align data platform capabilities with organizational goals • Mentor Data Engineers, conduct technical reviews, and promote engineering best practices across the team • Apply AI-assisted engineering practices to improve development efficiency, delivery quality, and platform sustainability