Junior AI/ML Engineer, GenAI, AWS

Job not on LinkedIn

🔥 0 minutes ago

🌐 Ukraine, Spain, +5 more countries – Remote

🇺🇦 Ukraine – Remote 🇪🇸 Spain – Remote 🇸🇰 Slovakia – Remote 🇷🇸 Serbia – Remote 🇦🇲 Armenia – Remote 🇸🇮 Slovenia – Remote 🇵🇱 Poland – Remote

⏰ Full Time

🟢 Junior

🤖 Machine Learning Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Provectus

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

Provectus is an artificial intelligence consultancy and solutions provider that helps businesses transform through AI. Offering both a use case and a platform approach, Provectus integrates AI into organizations to achieve unique business objectives and technical capabilities. Their solutions are cloud-native, vendor-agnostic, and open, allowing for deployment in customer's cloud without restrictive licenses. With applications in industries like retail, manufacturing, and healthcare, Provectus delivers AI-powered use cases and turnkey solutions to drive innovation and efficiency. They also offer consulting, customization, and managed AI services.

📋 Description

• Work in a pair with an FDE and an FDX • Build and ship production GenAI systems into customer environments, including cloud-native data, LLM-based, and agentic AI solutions • Build and optimize RAG systems for production use cases • Build the evaluation harness before building the feature • Write production code across AI, backend services, and data pipelines using tools suited to each customer • Integrate AI components into backend services and RESTful APIs • Take systems to production on AWS, or GCP/Azure where required by the customer • Implement LLMOps and AgentOps practices, including agent tracing, prompt and version management, cost and latency monitoring, regression testing, and drift detection • Start from blueprints and contribute to enablement and handover through documentation, runbooks, and pairing with client engineers • Feed reusable components and lessons back into Provectus Blueprints • Participate in technical discussions and architectural decisions • Conduct model evaluation, improve identified failure modes, and optimize model performance, efficiency, and reliability • Mentor junior and mid-level AI engineers • Conduct code reviews and share knowledge through documentation, presentations, and workshops

🎯 Requirements

• Proactive and self-directed; pushes for clarity rather than waiting for a ticket • Excellent communication and problem-solving skills • Comfortable with some ambiguity, with support from senior team members • B2+ English, comfortable collaborating across distributed, multicultural teams • Hands-on experience building or contributing to RAG systems, ideally in production or near-production • Solid engineering fundamentals; Python and/or TypeScript proficiency • Ability to become productive in an unfamiliar codebase with some ramp-up support • Practical AWS experience, such as 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 and contributing to or running test/evaluation cycles • Basic working knowledge of model/agent monitoring concepts • Awareness of cost and latency trade-offs when working with LLMs • Hands-on exposure to the Claude ecosystem is a plus, or strong ability to ramp up quickly • Practical experience with LLM APIs such as Anthropic, AWS Bedrock, or OpenAI in real projects • 2+ years of software or ML engineering experience, including exposure to production systems • Solid AI/ML foundations and understanding of common model failure modes • Hands-on production experience with the Claude ecosystem: Claude Code, CLAUDE.md, hooks, and skills files • Spec-driven development is a strong plus • Understanding of why an agent would prefer MCP to a REST integration; authoring an MCP server is a plus • Experience in financial services, insurance, or healthcare is nice to have • Consulting, professional services, or embedded customer-facing delivery experience is nice to have • AWS and Claude Code certifications, or actively pursuing them, are nice to have • Interest in agent-to-agent interoperability concepts is nice to have • CI/CD pipeline experience with GitHub Actions or GitLab CI is nice to have • Practical experience with NLP, LLMs, or recommendation engines is nice to have • Experience in Go, TypeScript, or Rust is nice to have • Experience with Apache Spark, Apache Airflow, or Kafka is nice to have

🏖️ Benefits

• Remote-friendly culture • Internal training programs with full support for Claude, AWS, and other professional certifications • Conference attendance • Career growth and active engineer development • Access to the latest AI tools and premium subscriptions • Long-term B2B collaboration • Private medical insurance or a budget for medical needs • Paid sick leave, vacation, and public holidays • Equipment and all the technology needed for comfortable, productive work