Own your opportunity to turn data into measurable outcomes for our customers’ most complex challenges. As an AI Engineer, you will be responsible for designing, developing and deploying AI models, algorithms and systems to enhance productivity, decision-making and automation within the organization . Here, you’ll see the bigger picture on mission initiatives and where your program management career can go at GDIT. MEANINGFUL WORK AND PERSONAL IMPACT As an AI Engineer, the work you’ll do at GDIT will be impactful to the mission. Architect and implement production-grade multi-agent AI solutions with modern orchestration frameworks, enabling reliable, transparent, and secure agentic workflows Build and maintain end-to-end agentic AI pipelines, from data ingestion and embedding to deployment and continuous evaluation, optimized for reusability and scalability Develop, test, and deploy internal AI applications that enhances people-powered, AI-enabled productivity Provide unified observability with logging, metrics, and alerts and analyze agent runtime behavior to tune latency, accuracy, and cost in production Integrate agentic AI services with existing enterprise systems and uphold AIOps practices for consistent deployment and scaling Collaborate with cross-functional teams including data scientists, software engineers, and product / service owners to align AI projects with business goals Must stay updated on emerging AI related technologies and recommend improvements to existing AI systems WHAT YOU’LL NEED TO SUCCEED Bring your expertise and drive for innovation to GDIT. The AI Engineer must have: Education: Bachelor’s Degree in Computer Science, Computer Engineering, Data Science or a related field Experience: 5+ years of experience in AI and machine learning, including hands-on experience with designing, developing, and deploying AI models and systems. 1+ years of experience developing products or functional prototypes using RAG and agentic AI technologies. Role requirements: Proven track record of delivering end-to-end AI projects and successfully integrating AI solutions into business processes. Proficiency in programming languages such as Python, R, or Java, and deep understanding of machine learning frameworks like TensorFlow or PyTorch. Strong hands-on experience with MCP, LangGraph, LlamaIndex, or similar agentic frameworks. Deep understanding of prompt or context engineering, tool definition, and state management for agents. Experience with cloud computing platforms, such as Azure (Preferred), OCI, AWS, or Google Cloud, including AI services like Azure AI Foundry, Bedrock, or Vertex AI. Strong background in working with large datasets and performing data preprocessing, feature engineering, and model evaluation.
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Job Type
Full-time
Career Level
Mid Level