JUNIOR AI/ML ENGINEER (GENAI, AWS)

Requirements: Mindset • Proactive and self-directed; you push for clarity rather than waiting for a ticket. • Excellent communication and problem-solving skills. • Comfortable with some ambiguity, with support from senior team members as you take on more. • B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth • Hands-on experience building or contributing to RAG systems, ideally in a production or near-production setting. • Solid engineering fundamentals; Python and/or TypeScript proficiency. Productive in an unfamiliar codebase with some ramp-up support. • Practical AWS experience (Lambda, S3, ECS, or similar); ready to grow into Bedrock and Bedrock AgentCore . • Some experience with containers and CI/CD in real projects. • Exposure to evaluating non-deterministic systems — you've contributed to or run test/eval cycles, even if you haven't owned a full eval suite end-to-end. • Basic working knowledge of model/agent monitoring concepts. • Awareness of cost and latency trade-offs when working with LLMs. • Some hands-on exposure to the Claude ecosystem (Claude Code, CLAUDE.md, hooks, skills files) is a plus, or strong ability to ramp up quickly. • Practical experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) in real projects. • 2+ years of software or ML engineering experience, including some exposure to production systems. • Solid AI/ML foundations — you understand what the models do well enough to reason about common failure modes. Nice to Have: • Experience in one of the industries: financial services, insurance, healthcare. • Consulting, professional services, or other embedded customer-facing delivery. • AWS and Claude Code Certifications (or actively pursuing them). • A2A: Interest in agent-to-agent interoperability concepts. • CI/CD pipeline experience (GitHub Actions, GitLab CI). • Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. • Experience in an additional language (Go, TypeScript, or Rust). • Experience with Apache Spark, Apache Airflow, Kafkа. • Experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build. • MCP: you can say why an agent would prefer it to a REST integration, having authored a server is an additional plus. Responsibilities: • Build and contribute to RAG system components under senior guidance, with growing autonomy. • Write tests and help build out evaluation harnesses for the features you work on. • Write production code across the stack (AI, backend services, data pipelines) with code review support. • Help integrate AI components into backend services and RESTful APIs. • Support deployment of systems to AWS (containerized, CI/CD), taking on more of this independently over time. • Contribute to documentation, runbooks, and client handover materials. • Participate in technical discussions and architectural decisions, with an eye toward taking on more of this independently. • Support model evaluation efforts and help investigate and improve failure modes. • Take on increasing ownership of components and technical decisions as you grow in the role. What We Offer: • The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment • A forward-deployed model working in small, senior teams alongside FDE and FDX • A growing AI delivery practice where you help build the tooling and frameworks, not just use them • Remote-friendly culture • Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance • Career growth; we actively develop our engineers • Access to the latest AI tools and premium subscriptions • Long-term B2B collaboration • Private medical insurance or a budget for your medical needs • Paid sick leave, vacation, and public holidays • Equipment and all the tech you need for comfortable, productive work How we hire: • Intro conversation. The role, your background and aspirations, tech questions. • Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant • HR Interview. Soft skills and expectations • HM interview. Tech questions; a live engineering session is also possible