AI Engineer

Requirements:

• We expect at least 4 years of professional software development experience, with a track record of delivering real production features.
• We value genuine interest in AI and agentic systems, including hands-on experimentation with LLMs or agents through side projects, hackathons, or personal prototypes.
• We look for strong engineering fundamentals, including distributed systems basics, API development, and cloud infrastructure experience with AWS.
• We want someone who understands what strong code and solid design look like in a production environment.
• We appreciate people who can experiment quickly, iterate on ideas, and bring fresh perspectives.
• Nice to have: exposure to multiple LLM APIs or agent frameworks.
• Nice to have: Python or Go proof-of-concepts for AI, automation, or data work.
• Nice to have: contributions to open-source AI/ML tooling.

Responsibilities:

• We build agentic features hands-on, working with senior engineers to design and deliver AI-powered remediation capabilities, including agent workflows, retrieval pipelines, and evaluation loops.
• We write production code every day, primarily using Java and Spring Boot microservices with AI integrations on AWS.
• We help take ideas from design documents through to production, including instrumentation, monitoring, and continuous improvement based on real usage.
• We bring a current perspective on new models, frameworks, and techniques to the team.
• We work closely with senior engineers, strengthening our technical depth and taking on greater ownership over time.

Technologies:

• AI
• API
• AWS
• Cloud
• Java
• LLM
• MCP
• Python
• RAG
• Spring
• Spring Boot
• microservices
• Lambda
• CloudWatch
• IAM

More

We are hiring for a client engagement with HP and are looking for two US-based native candidates to join on a day-one onsite arrangement. Our environment is centered on Java and Spring Boot microservices running on AWS, with an AI stack that includes AWS Bedrock and AgentCore, MCP for tool integration, RAG pipelines with vector and graph-based retrieval, and LLM-as-judge evaluation. We also use Python and Go in smaller supporting areas, and we value fast learning, curiosity, and practical experience with modern AI systems.

last updated 44 week of 2026