AI Engineer

Key Responsibilities

• Write production-grade code and contribute to scalable, maintainable software systems
• Design modular, extensible architectures that support AI integration within enterprise platforms
• 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

Requirements

• 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 AI/ML Frameworks & Tools: TensorFlow, 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, GitLab CI, GitHub 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: PostgreSQL, MySQL, NoSQL: MongoDB, Elasticsearch, DynamoDB
• Hands-on experience with Amazon Web Services (AWS) (e.g., S3, EC2, Lambda, SageMaker)
• Experience with MLOps tools for model deployment, monitoring, and lifecycle management

Preferred Qualifications

• 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 performance, scalability, and cost efficiency
• Exposure to natural language processing (NLP), especially in document or report generation systems
• Experience supporting intelligence, analytics, or mission-focused platforms