Senior GenAI Software Developer

Senior GenAI Software Developer

Pay: 70-75/hr (6+ month contract)

Location: Plano, TX (hybrid role)

Responsible for designing and developing cloud-native AI infrastructure using Python, AWS services (Lambda, ECS, API Gateway, DynamoDB), LLMs, RAG systems, and vector databases. The ideal candidate will build scalable and secure APIs, implement AI agents, and develop enterprise-grade retrieval systems leveraging AWS's AI/ML services.

Essential Functions
• Design and implement serverless architectures using AWS Lambda, API Gateway, and event-driven patterns
• Develop and optimize AI solutions using LLMs, RAG frameworks, and vector databases (OpenSearch)
• Build and maintain data pipelines for training, fine-tuning, and serving AI models
• Create scalable APIs using Python, FastAPI, and AWS API Gateway
• Implement agent-based systems that can interact with various data sources and services
• Design and optimize DynamoDB schemas for AI-driven applications
• Deploy and manage containerized applications using AWS ECS and ECR
• Develop knowledge base solutions using AWS Kendra and custom vector stores
• Optimize prompt engineering techniques for improved LLM performance
• Implement security best practices for AI systems including input validation and output filtering
• Create monitoring and observability solutions for AI applications
• Build CI/CD pipelines for reliable deployment of AI services
• Collaborate with ML engineers to optimize model serving infrastructure
• Implement robust error handling and fallback mechanisms for AI systems
• Document API specifications and system architectures

Requirements
3+ years experience in development with a focus on API's. Monoliths or Microservices.
Experience and Knowledge of modern Server development languages such as Python, or Node.
Strong experience with AWS services (Lambda, ECS, API Gateway, DynamoDB)
Proficiency with container technologies (Docker) and orchestration
Familiarity with vector databases and embedding models
Knowledge of AI/ML concepts and LLM integration patterns
Understanding of RAG systems and retrieval optimization techniques
Experience with CI/CD pipelines and infrastructure as code
Familiarity with security best practices for cloud applications

Preferred Qualifications
Experience with AWS Bedrock, Amazon Titan, or other AWS AI services
Familiarity with AWS CDK or Terraform for infrastructure as code
Experience with OpenSearch or Elasticsearch for vector search
Knowledge of AWS security services (IAM, Cognito, KMS)

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