Junior AI/ML Engineer (GenAI, AWS)

Requirements

Mindset

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• Proactive and self-directed; you push for clarity rather than waiting for a ticket.
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• Excellent communication and problem-solving skills.
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• Comfortable with some ambiguity, with support from senior team members as you take on more.
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• B2+ English, comfortable collaborating across distributed, multicultural teams.
• Technical depth
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• Hands-on experience building or contributing to RAG systems, ideally in a production or near-production setting.
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• Solid engineering fundamentals; Python and/or TypeScript proficiency. Productive in an unfamiliar codebase with some ramp-up support.
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• Practical AWS experience (Lambda, S3, ECS, or similar); ready to grow into Bedrock and Bedrock AgentCore .
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• Some experience with containers and CI/CD in real projects.
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• 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.
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• Basic working knowledge of model/agent monitoring concepts.
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• Awareness of cost and latency trade-offs when working with LLMs.
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• 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.
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• Practical experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) in real projects.
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• 2+ years of software or ML engineering experience, including some exposure to production systems.
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• Solid AI/ML foundations — you understand what the models do well enough to reason about common failure modes.

Nice to Have

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Experience in one of the industries: financial services, insurance, healthcare.

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Consulting, professional services, or other embedded customer-facing delivery.

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AWS and Claude Code Certifications (or actively pursuing them).

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A2A: Interest in agent-to-agent interoperability concepts.

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CI/CD pipeline experience (GitHub Actions, GitLab CI).

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Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.

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Experience in an additional language (Go, TypeScript, or Rust).

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Experience with Apache Spark, Apache Airflow, Kafkа.

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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.

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MCP: you can say why an agent would prefer it to a REST integration, having authored a server is an additional plus.

Responsibilities

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• Build and contribute to RAG system components under senior guidance, with growing autonomy.
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• Write tests and help build out evaluation harnesses for the features you work on.
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• Write production code across the stack (AI, backend services, data pipelines) with code review support.
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• Help integrate AI components into backend services and RESTful APIs.
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• Support deployment of systems to AWS (containerized, CI/CD), taking on more of this independently over time.
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• Contribute to documentation, runbooks, and client handover materials.
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• Participate in technical discussions and architectural decisions, with an eye toward taking on more of this independently.
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• Support model evaluation efforts and help investigate and improve failure modes.
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• Take on increasing ownership of components and technical decisions as you grow in the role.

What We Offer

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• The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment
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• A forward-deployed model working in small, senior teams alongside FDE and FDX
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• A growing AI delivery practice where you help build the tooling and frameworks, not just use them
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• Remote-friendly culture
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• Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance
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• Career growth; we actively develop our engineers
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• Access to the latest AI tools and premium subscriptions
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• Long-term B2B collaboration
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• Private medical insurance or a budget for your medical needs
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• Paid sick leave, vacation, and public holidays
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• Equipment and all the tech you need for comfortable, productive work

How we hire

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• Intro conversation. The role, your background and aspirations, tech questions.
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• Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
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• HR Interview. Soft skills and expectations
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• HM interview. Tech questions; a live engineering session is also possible