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