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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Caci Inc - **Location:** King of Prussia, PA, United States - **Experience:** Expert - **Salary:** $82,100.0 - $172,400.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Artificial Neural Networks, Computer Vision, C Sharp (Programming Language), C++ (Programming Language), Nvidia CUDA, Extract Transform Load (ETL), Linux, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Cloud Services, Software Deployment, Software Engineering, Virtualization Technology, Reinforcement Learning, Rust (Programming Language), Test-Driven Development (TDD), Large Language Models, Apache Spark, Gitlab, Git, Pandas, Containerization, Pyspark, Kubernetes, Information Technology, HuggingFace, Rancher, Bitbucket, Machine Learning Operations, Functional Programming, Data Delivery, GPT, Software Version Control, Data Pipelines, Devsecops, Software Library, Docker, Golang - **Published:** August 10, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9104880/data-engineer ## About the Role * Active TS/SCI Clearance * B.S. in Data Science, AI/ML, Computer Science, or related field * 5+ years of experience, preferably as Data Engineer/Scientist, Machine Learning Engineer, or Software Development * Experience developing data pipelines and normalizing data with canonical Python packages (e.g. NumPy, Pandas, Polars) * Experience building ETL pipelines with either PySpark/Spark or Temporal.io workflows * Experience contributing on a team using version control (e.g. git, GitLab, Bitbucket) Desired: * M.S. or PhD in Data Science, AI/ML, Computer Science, or related field * Experience with Gitlab, DevSecOps utilizing test-driven development, containers, (e.g. Docker, Docker Compose), cloud services (e.g. AWS) * Active TS/SCI U.S. Government Security Clearance with Polygraph * Experience leading an interdisciplinary team of researchers and software developers * Experience with any of the following: * Large Language Models and experience identifying ways to incorporate them into new domains and applications * Applying Transformer-based architectures to domains in other areas outside of Natural Language Processing (NLP) such as computer vision * Natural Language Processing algorithms such as BERT * Reinforcement learning and familiarity with Gymnasium Gym, OpenEnv, TorchRL, RLlib, and Stable Baselines * Applying clustering algorithms and/or deep neural networks to real life problems * Implementing tracking and pattern-of-life algorithms * Experience with GenAI Ops techniques (e.g. LLM-as-a-judge) and frameworks (e.g. LangFuse, MLFlow, Arize Phoenix) * Experience with Machine Learning libraries and frameworks such as HuggingFace and LangChain * Experience with Linux * Familiarity with using AWS cloud computing resources such as EC2, S3, Lambda, Bedrock, etc. * Experience with any of the following additional languages: Java, C++, Rust, Go, and/or C# * Experience implementing algorithms on the GPU in Python or C++ using CUDA and other CUDA libraries * Experience in application deployment, virtualization, and containerization (e.g. Podman, Docker, Kubernetes, Rancher) * Experience shaping and writing proposals ## Description You will work within a multidisciplinary team consisting of both developers and researchers. The team's core focus is the implementation of ETL pipelines to support a variety of AI/ML and LLM solutions, which in turn address a broad range of customer challenges. Responsibilities also include: * Assembling large, complex sets of data to support AI/ML algorithm implementation * Building required infrastructure for optimal extraction, transformation and loading of data from various data sources * Curating and maintaining data that is stored in support of metrics and evaluation * Implementing Artificial Intelligence/Machine Learning algorithms * Identifying designs and implementing internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes * Using Agile methodologies to develop software. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Vectorize all the things! 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