Note: The job is a remote job and is open to candidates in USA. ExpediteInfoTech is a trusted federal contractor focused on leveraging emerging technologies to modernize systems across government agencies. The role of the AI Engineer emphasizes developing secure and scalable AI applications with a focus on backend processes and integration of advanced AI methodologies.
Responsibilities
- Expert hands-on building of RAG and Graph-RAG architectures to handle multiple complex data formats (PDF, images, tables, Word documents, Excel, acronyms, attachments, etc.) to create cleansed standardized data for hydration into a vector database
- Expert hands-on knowledge on text embeddings, image embeddings, chunking logic, metadata creation, and embedding vectors indexing
- Expert hands-on knowledge in creating a highly accurate RAG retrieval system with knowledge on reranking, semantic search, similarity search, hybrid search, etc., to search by text or images
- Implement secure, scalable, highly accurate RAG, Agentic AI pipelines using LangChain, Strands, MCP, A2A frameworks, or AWS-native services like Bedrock, Agentic Core, OpenSearch Vector Database, and Knowledgebase
- Create backend infrastructure for chatbot applications with long-term and short-term memory capabilities to improve user experience
- Hands-on knowledge of creating APIs, Graph-RAG, develop agentic AI workflows with MCPs, A2A, and Skills
- Experience operationalizing AI/ML pipelines in SageMaker Studio with model governance
- Experience with Amazon - Bedrock, Agentic Core, OpenSearch Vector Database, knowledgebase, lambda, API Gateway, FASTAPIs or Flask, SQS, SNS, Step functions, DynamoDB, RDS/Postgres SQL, ECS, ECR, IAM, CloudWatch, and EKS or Fargate
- Integrate multiple LLMs via APIs (AWS Bedrock: Anthropic - Claude, Titan, Llama, Stability Diffusion models)
- Implement structured prompt engineering frameworks, response evaluation tools, and feedback loops
- Build model optimization layers, including prompt selectors, model switchers, and cache layers
- Deploy AI services using SageMaker, ECS, Lambdas, Agentic Core, and Elastic Load Balancers
- Containerize backend systems with Docker and deploy to scalable environments using ECS/EKS
- Implement CI/CD pipelines via GitHub Actions integrated with AWS Systems Manager and CodePipeline
- Architect solutions for VPC isolation, IAM hardening, and FedRAMP High compliance
- Integrate AI workflows with enterprise databases, legacy platforms, and identity providers
- Monitor service performance, GPU utilization, and system health via CloudWatch and custom logging
- Build automated testing pipelines for model accuracy, bias detection, and system robustness
- Maintain technical documentation and developer runbooks for long-term system support
Skills
- 10+ years of IT experience
- 3+ years of experience as an AI Engineer
- 3+ years of experience in AWS
- AWS Services: Graph RAG, Bedrock Agentic Core, Agentic AI, EC2 (GPU-enabled), SageMaker (Studio, Pipelines, Endpoints, Model Registry), Bedrock, OpenSearch Vector DB, Systems Manager, Load Balancers, Amazon - Bedrock, OpenSearch Vector Database, knowledgebase, lambda, API Gateway, FASTAPIs or Flask, SQS, SNS, Step functions, DynamoDB, RDS/Postgres SQL, and EKS or Fargate
- Proficient in coding: Python (async, FastAPI, LangChain, Transformers) and Terraform
- DevSecOps: Docker, GitHub, GitHub Actions, CI/CD pipelines
- Cloud-Native Development: Infrastructure-as-Code, cloud monitoring, and security policies
- Experience with React or other frontend frameworks for full-stack AI interfaces (Streamlit, ReactJS, JavaScript, Typescript, HTML, and CSS)
- Government/federal sector AI solution experience with FedRAMP High or FISMA
- Bachelor's or equivalent in Computer Science, Software Engineering, AI/ML, or related technical field
- AWS certifications (Machine Learning Specialty, Solutions Architect) a strong plus
- Experience using AI coding assistant tools like OpenAI Codex and Claude Code
Company Overview