Senior AI/ML Engineer, GenAI, AWS

🔥 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

🟠 Senior

🤖 Machine Learning Engineer

👻 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 • Integrate AI components into backend services and RESTful APIs • Deploy systems to AWS, or GCP/Azure when required by the customer, using containers and CI/CD • 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 inheriting client engineers • Feed reusable components and lessons back into Provectus Blueprints • Participate in technical discussions and architectural decisions • Conduct model evaluation, improve 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; push for clarity rather than waiting for a ticket • Excellent communication and problem-solving skills • Comfort with ambiguity and ownership • B2+ English, comfortable collaborating across distributed, multicultural teams • 5+ years in software or ML engineering, with production systems you were accountable for • Solid AI/ML foundations and ability to reason about model failure modes • Shipped production LLM applications and agentic workflows, not demos, POCs, or notebooks • Experience with multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure • Experience with LLM APIs such as Anthropic, AWS Bedrock, or OpenAI and agent frameworks • Experience building and optimizing RAG systems in production • Strong engineering fundamentals; full-stack mindset across AI, backend development, and cloud infrastructure • Python and/or TypeScript proficiency • Hands-on AWS production experience, including Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar • Cloud-native delivery experience with containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines • Built or owned an evaluation suite for a non-deterministic system, including ground truth and release gates • Model and agent monitoring and drift detection experience • Cost and latency optimization experience, including model tiering and caching • Hands-on production experience with the Claude ecosystem, including Claude Code, CLAUDE.md, hooks, and skills files • MCP knowledge, including why an agent would prefer it to a REST integration • Additional experience listed as nice to have: financial services, insurance, or healthcare; consulting or embedded customer-facing delivery; AWS and Claude Code certifications; A2A; CI/CD pipelines; NLP, LLMs, or recommendation engines; Go, TypeScript, or Rust; Apache Spark, Apache Airflow, or Kafka

🏖️ Benefits

• Remote-friendly culture • Internal training programs with full support for Claude, AWS, and other professional certifications • Conference attendance • Career growth; 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 tech needed for comfortable, productive work