Sr. Software Engineer - Core Automation Platform
$110,700–$171,800 year
HybridAshburn, Virginia, United States
Job Summary
Design and build reusable GenAI platforms, SDKs, and pipelines using agent frameworks like LangGraph and LangChain to accelerate AI adoption across engineering teams. Develop production-ready AI applications with Python, FastAPI, and React, implementing RAG architectures, LLM orchestration layers, and LLMOps processes on AWS services including Bedrock and SageMaker. Collaborate with global teams to solve complex technical challenges, optimize incident detection and response, and ensure secure, enterprise-grade AI solutions in production. Implement CI/CD automation, containerization, and observability strategies while partnering with cybersecurity teams. Hybrid work model requires at least 3 days in the office; salary range is $110,700 to $171,800 USD.
Required Qualifications
- 2+ years of relevant work experience
- Bachelors degree
- 5+ years of relevant work experience
- Hybrid Work Model: Visa requires at least 3 days in office
- Travel 5-10% of the time
- Experience working within Agile development environments, including SAFe and globally distributed teams
Desired Qualifications
- 2-5 years of work experience with a Bachelor's Degree
- more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
- Bachelor's or Master's in Computer Science, Engineering, AI/ML, or related field
- Experience working within Agile development environments, including SAFe and globally distributed teams
- Generative AI frameworks and tools such as LangGraph, LangChain, Haystack, AWS Bedrock, or similar platforms
- Backend development using Python, with FastAPI strongly preferred
- Frontend development using React and TypeScript
- Event-driven architectures and messaging technologies such as Kafka/MQ/SQS
- Vector databases and retrieval technologies such as FAISS, Pinecone, Weaviate, OpenSearch, or similar solutions
- CI/CD automation and developer platforms, including GitHub, Bitbucket, Jira, Jenkins, ServiceNow, and related tooling
- Distributed systems, microservices architecture, and API design
- Retrieval-Augmented Generation (RAG) implementations at scale using cloud-based or enterprise vector stores
- Building and deploying solutions on AWS, including Lambda, ECS/EKS, S3, API Gateway, Bedrock, and SageMaker
- Event-driven architectures and messaging technologies such as Kafka or Kinesis
- Developing reusable platforms, frameworks, or developer tools that accelerate adoption across engineering teams
- Payments, financial services, risk management, fraud analytics, or other highly regulated industries
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