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.

• AI Solution Design & Development
  • 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.
• Software Architecture & Engineering
  • 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.
• Data Engineering & Large-Scale Processing
  • 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.
• Model Development & Deployment
  • 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.
• Collaboration & Communication
  • Serve as a technical liaison across engineering, data, and mission stakeholders.
  • Clearly communicate AI approaches, trade-offs, and system design decisions to both technical and non-technical audiences.
• Continuous Innovation
  • Stay current with emerging AI/ML technologies, frameworks, and enterprise data solutions.
  • Identify opportunities to enhance system performance, automation, and intelligence capabilities.

• Required Technical Skills
  • 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.
• AI/ML Frameworks & Tools
  • 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.
• Enterprise Software & Architecture
  • Experience designing or contributing to scalable software architectures, including microservices-based architecture.
  • Experience with 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.).
• Data Engineering & Big Data Technologies
  • 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, Dynamo
    
    DB).
• Cloud & MLOps
  • 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.
• Preferred Qualifications
  • Experience building AI-enabled systems from the ground up in enterprise environments.
  • Familiarity with data governance, security, and compliance in…