Dice is the leading career destination for tech experts at every stage of their careers. Our client, BayOne Solutions, is seeking the following. Apply via Dice today!
About the role:
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.
Required Qualifications
- 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.
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