Junior AI/ML Engineer (GenAI, AWS)

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Provectus is an AWS Premier Partner and an Anthropic Strategic Partner , working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.

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Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.

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Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.

Where this role sits

You will work in a senior pod alongside an FDE, an FDX, and Senior AI Engineers — contributing to real delivery work while building toward independent ownership.

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

Originally posted on Himalayas