Software Engineer

We're building out a high-caliber engineering team to support a new platform. We're looking for a Senior Software Engineer who is equally strong in Java and Python, has built and operated microservices on AWS with mature CI/CD pipelines, and has hands-on experience integrating AI/ML capabilities into production systems. This role starts as a contract with a path to full-time conversion based on performance and fit.

What you'll do

• Design, build, and own backend microservices in Java and Python running on AWS.
• Architect scalable, fault-tolerant distributed systems with clean service boundaries and well-designed APIs.
• Build and maintain robust CI/CD pipelines automated testing, deployment, observability, rollback strategies.
• Integrate AI/ML capabilities (LLMs, model APIs, RAG, embeddings, etc.) into product features and backend services.
• Operate services end-to-end on AWS compute, networking, storage, IAM, monitoring, cost management.
• Stay current on the rapidly evolving AI landscape and bring relevant advances into the stack.
• Collaborate cross-functionally with product, ML, and platform teams during ramp-up.

• 6 9 years of professional software engineering experience.
• Strong production experience in both Java and Python (Spring Boot, FastAPI/Flask, or equivalent).
• Proven track record designing and operating microservices architectures in production.
• Deep AWS expertise EKS/ECS, Lambda, API Gateway, RDS/DynamoDB, S3, SQS/SNS, CloudWatch, IAM.
• Hands-on CI/CD experience Jenkins, GitHub Actions, GitLab CI, AWS CodePipeline, or similar; IaC with Terraform or CloudFormation.
• Practical AI/ML integration experience working with LLM APIs (OpenAI, Anthropic, Bedrock), RAG pipelines, vector databases, or embedding-based features in production.
• Strong fundamentals: distributed systems, API design, testing strategy, observability, security.
• Working knowledge of current AI/LLM trends you can speak fluently to what's changed in the last 6 months.

Nice to have

• Kubernetes / container orchestration in production.
• Event-driven / streaming architectures (Kafka, Kinesis).
• Experience with agentic AI systems, prompt engineering, or fine-tuning workflows.
• MLOps tooling exposure (MLflow, SageMaker, Bedrock).
• Performance tuning of JVM and Python services at scale.