Artificial Intelligence (AI) Engineer - Backend Focus

Job Title: Artificial Intelligence (AI) Engineer - Backend Focus

Client: Federal Government

Location: Remote with occasional travel to the client site in Baltimore. Candidates must currently live within a commutable distance of the office.

Employment Type: W2 on ExpediteInfoTech's payroll. This position requires a Permanent resident or a U.S. citizen. The selected candidate will go through a Public Trust Clearance process.

About ExpediteInfoTech: ExpediteInfoTech is a trusted federal contractor focused on leveraging emerging technologies to modernize systems, enhance security, and drive operational efficiency across government agencies. We work with clients across AI/ML, RPA, Cloud, Enterprise Architecture, Cybersecurity, Health IT infrastructure, and Federal Financial systems—all delivered through a hands-on, collaborative, results-driven approach. Our core values are collaborative innovation, quality service, and exceeding expectations. https://expediteinfotech.com/)

Position Summary: A backend-focused AI engineer responsible for developing secure, scalable, and production-grade AI applications, with deep experience in LLM integration, retrieval-augmented generation (RAG) pipelines, including Graph-RAG, Agentic AI, and cloud-based LLM Ops workflows. The role emphasizes Amazon SageMaker Studio, ECS, ECR, lambdas, Agentic Core, APIs, OpenSearch Vector DB, and Dynamo DB for operationalizing GenAI-powered Digital Products within FedRAMP-compliant AWS environments.

Responsibilities:

AI Solution Development:

• 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.

AI/ML 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.
• Frameworks: LangChain, LangFuse, LlamaIndex, Strands, RAGAS, CrewAI, MCP, and A2A.
• Prompt engineering, LLM evaluation methodologies, bias detection, and hallucination detection.

LLM Integration & LLM Ops:

• 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

Cloud Infrastructure & Deployment:

• 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

System Integration & Maintenance:

• 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

Work Environment:

• Remote-first with collaborative engagements and occasional client travel
• Mission-focused development aligned with executive priorities
• Continuous learning and rapid prototyping of cutting-edge AI technologies
• Agile delivery culture with strong cross-functional collaboration

Required:

Required / Minimum Qualifications

• 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

Preferred:

Preferred / Nice-to-Have Qualifications

• 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.