Senior AI/ML Engineer

CGI•Reston, VA

1d•$89,600 - $211,600•Hybrid

About The Position

We are looking for a Senior AI Software Engineer to design and deliver production-ready AI applications powered by large language models and foundation models. This role focuses on building scalable, enterprise-grade solutions using AWS Bedrock and other AI platforms, integrating advanced AI capabilities into real-world products. We partner with 15 of the top 20 banks globally, and our top 10 banking clients have worked with us for an average of 26 years!. This role is located at a client site in Reston, VA. A hybrid working model is acceptable. You will work across AI engineering, cloud architecture, and backend development to create intelligent systems that support automation, reasoning, and data-driven decision-making. This is a hands-on position suited for someone who enjoys taking AI concepts from experimentation to fully deployed, reliable applications.

Requirements

6+ years of overall software engineering experience
3+ years of hands-on experience building AI/ML-powered applications
Strong experience working with large language models (LLMs) and foundation model APIs
Solid programming skills in Python, along with at least one backend language (Java, TypeScript, or Go)
Hands-on experience building real-world AI solutions such as RAG systems, copilots, chatbots, or workflow automation tools
Familiarity with AWS ecosystem, especially Bedrock, S3, Lambda, API Gateway, ECS/EKS, and Step Functions
Experience designing scalable APIs and microservices for AI-driven applications
Comfortable working with vector databases (e.g., FAISS, OpenSearch, pgvector) and semantic search techniques
Practical knowledge of embeddings, prompt engineering, and model evaluation approaches
Exposure to frameworks like LangChain, LlamaIndex, or similar orchestration tools
Experience with containerization and deployment using Docker and Kubernetes
Understanding of monitoring and observability for AI systems (latency, cost, hallucination detection, etc.)
Ability to design and optimize AI pipelines, including ingestion, indexing, and evaluation
Working knowledge of AI safety practices, guardrails, and prompt management
Familiarity with MLOps concepts and tools such as AWS SageMaker
Comfortable navigating multi-model environments and selecting the right model for the task
Strong problem-solving mindset with a systems-level approach to architecture

Responsibilities

Work across AI engineering, cloud architecture, and backend development to create intelligent systems that support automation, reasoning, and data-driven decision-making.
Taking AI concepts from experimentation to fully deployed, reliable applications.