Junior AI/ML Engineer (GenAI, AWS)

Role overview Contribute to applied AI delivery in a small, senior engineering team, building toward independent ownership of components and technical decisions. The work spans retrieval-augmented generation (RAG), backend services, data pipelines, and deployment for enterprise use cases in areas including financial services and healthcare.

Responsibilities - Build RAG components and production software across AI, backend, and data pipeline layers, with code review and senior guidance. - Write tests and contribute to evaluation tools for the features you develop. - Integrate AI components with backend services and REST APIs. - Help deploy containerized systems to AWS and support CI/CD workflows. - Evaluate model behavior, investigate failure modes, and help improve system performance. - Maintain documentation, operational runbooks, and client handover materials; contribute to technical discussions as your ownership grows.

Requirements - At least two years of software engineering or related professional experience. - Hands-on experience building or contributing to RAG systems, ideally in production or near-production work. - Solid engineering fundamentals and proficiency in Python and/or TypeScript; able to work in an unfamiliar codebase with support. - Practical AWS experience, such as Lambda, S3, or ECS, plus some experience with containers and CI/CD. - Experience using LLM APIs in real projects and exposure to testing or evaluating non-deterministic systems. - Awareness of model or agent monitoring and the cost and latency trade-offs of LLM applications. - Proactive problem-solving, clear communication, comfort with some ambiguity, and B2-level or higher English for distributed collaboration.

Nice to have - Familiarity with AWS Bedrock, Bedrock AgentCore, or tools and workflows in the Claude ecosystem. - Experience with NLP, recommendation systems, additional programming languages, data tools such as Spark, Airflow, or Kafka, or CI/CD platforms such as GitHub Actions and GitLab CI. - Relevant cloud or AI certifications, or active study toward them; familiarity with agent interoperability or MCP.

Benefits and work setup - Remote-friendly work in small, forward-deployed teams, with long-term business-to-business collaboration. - Training and support for professional certifications, conference attendance, and career development. - Access to AI tools, equipment, and either private medical insurance or a medical budget; paid sick leave, vacation, and public holidays.