Mid Level Software Engineer, Full Stack and Generative AI

Title: Fullstack Developer GenAI Location : Rockville, MD Target Start Date : SAP Type: contract Pay Rate: DOE Full Stack Development

• Design and develop scalable full stack applications with Angular frontends and microservices-based backends
• Build secure, high-performance RESTful and GraphQL APIs using modern backend frameworks such as Java/Spring Boot and Python/FastAPI
• Develop responsive and accessible user interfaces using Angular and TypeScript
• Collaborate with cross-functional teams including data engineering, security, and business stakeholders to translate business requirements into technical solutions
• Utilize AI-assisted development tools responsibly while maintaining code quality and security best practices

Generative AI Integration

• Implement Generative AI solutions including LLM integrations, prompt engineering, and Retrieval-Augmented Generation (RAG) pipelines
• Partner with data science and engineering teams to integrate ML models and support model serving infrastructure
• Promote responsible AI practices including human oversight, validation, and bias monitoring

CI/CD & Infrastructure

• Design and maintain CI/CD pipelines using tools such as Jenkins
• Implement infrastructure-as-code and containerized deployments using Docker and Kubernetes
• Integrate automated testing, security scanning, and quality assurance into deployment pipelines

Technical Leadership

• Provide mentorship and technical guidance to junior engineers through code reviews and collaborative development
• Contribute to architecture discussions, engineering standards, and best practices
• Ensure systems align with enterprise security, compliance, audit, and governance standards

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or related field
• 5-7 years of professional software engineering experience
• Strong proficiency in Python and/or Java backend development
• 3 years of hands-on experience with Angular, TypeScript, RxJS, and state management frameworks such as NgRx
• Experience designing and implementing RESTful APIs and/or GraphQL services
• Hands-on experience with AWS services including Lambda, ECS/EKS, API Gateway, S3, RDS, and DynamoDB
• Experience with containerization technologies such as Docker and Kubernetes
• Proficiency with relational and NoSQL databases including PostgreSQL, MongoDB, and DynamoDB
• Strong understanding of application security principles including OWASP Top 10, secrets management, and least-privilege access
• t least 1 year of experience working with LLMs, prompt engineering, embedding models, or platforms such as OpenAI, Anthropic, or AWS Bedrock

Preferred Qualifications

• Experience within regulated or enterprise environments
• Experience building human-in-the-loop review systems, annotation platforms, or approval workflows for AI-generated outputs
• Familiarity with LangChain, LlamaIndex, or similar LLM orchestration frameworks
• Experience implementing RAG architectures using vector databases such as Pinecone, Weaviate, pgvector, or OpenSearch
• Familiarity with event-driven architectures and messaging systems including Kafka, AWS SQS/SNS, or Kinesis
• Exposure to observability and monitoring tools such as Datadog, Grafana, or CloudWatch
• Experience with microservices patterns including circuit breakers, service mesh, and distributed tracing
• Familiarity with feature flagging, canary deployments, and progressive delivery strategies
• Contributions to open-source projects or technical publications related to AI/ML

Key Competencies

• End-to-end ownership of features across frontend, backend, and database layers
• Strong debugging and troubleshooting skills across distributed systems
• bility to balance business priorities, scalability, and technical constraints
• Excellent communication and collaboration skills across technical and non-technical teams
• Commitment to automated testing, code quality, and engineering best practices
• bility to quickly learn and adopt emerging technologies, particularly within AI/ML and cloud ecosystems

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