AI Engineer

Overview

We have an immediate need for an Artificial Intelligence (AI) Engineer to support TO-005, Report Authoring and Dissemination (RAD). This role will work closely with system and software engineers to design, prototype, and integrate AI-driven capabilities into the existing RAD architecture—while also contributing to the design of next-generation architecture built for scalability and large data processing.

This is a transformative opportunity to build systems from the ground up that augment human intelligence, streamline workflows, and enable data-driven decision-making across enterprise environments handling high-volume, complex datasets.

• Lead end-to-end design and development of AI/ML solutions—from concept, prototyping, and architecture design to production deployment
• Write production-grade code and contribute to scalable, maintainable software systems
• Design modular, extensible architectures that support AI integration within enterprise platforms

• Contribute to or lead the design of enterprise-grade software architecture from scratch, including microservices and distributed systems
• Build backend services and APIs to support AI-driven applications and data pipelines
• Ensure systems are designed for scalability, fault tolerance, and high availability
• Implement best practices in software engineering, version control, CI/CD, and testing frameworks

• Design and implement data pipelines to ingest, process, and analyze large structured and unstructured datasets
• Perform Exploratory Data Analysis (EDA) to inform model design and data strategy
• Optimize data storage and retrieval for performance and scalability

• Develop, train, evaluate, and fine-tune machine learning and deep learning models
• Implement robust validation, testing, and monitoring to ensure model accuracy, fairness, and reliability
• Deploy models into production environments using MLOps best practices

• Serve as a technical liaison across engineering, data, and mission stakeholders
• Clearly communicate AI approaches, tradeoffs, and system design decisions to both technical and non-technical audiences

• Stay current with emerging AI/ML technologies, frameworks, and enterprise data solutions
• Identify opportunities to enhance system performance, automation, and intelligence capabilities

• Strong proficiency in Python (primary for AI/ML development)
• Experience with one or more backend/system languages:
  Java, Go, C++, or Scala
• Familiarity with SQL and working knowledge of query optimization for large datasets

• Experience with frameworks such as Tensor Flow, PyTorch, Scikit-learn, or Hugging Face
• Strong understanding of machine learning algorithms, deep learning, and data modeling techniques

• Experience designing or contributing to scalable software architectures, including:
  • Microservices-based architecture
  • Distributed systems and event-driven design
• Experience building and consuming RESTful APIs or gRPC services
• Familiarity with containerization (Docker) and orchestration tools like Kubernetes
• Experience implementing CI/CD pipelines (Jenkins, Git Lab CI, Git Hub Actions, etc.)

• Experience working with large-scale datasets and distributed processing frameworks such as:
  • Apache Spark, Hadoop, or Flink
• Familiarity with data streaming technologies (Kafka, Kinesis)
• Experience with databases:
  • Relational:
    Postgre
    
    SQL, MySQL
  • No
    
    SQL:
    Mongo
    
    DB, Elasticsearch, DynamoDB

• Hands‑on experience with Amazon Web Services (AWS) (e.g., S3, EC2, Lambda, Sage Maker)
• Experience with MLOps tools for model deployment, monitoring, and lifecycle management
• Understanding of infrastructure‑as‑code (Terraform, Cloud Formation) is a plus

• Experience building AI‑enabled systems from the ground up in enterprise environments
• Familiarity with data governance, security, and compliance in large‑scale systems
• Experience optimizing systems for…