Senior AI/ML Engineer, GenAI, AWS

🔥 16 hours ago

🌐 North Macedonia, Bosnia and Herzegovina, +5 more countries – Remote

🇲🇰 North Macedonia – Remote 🇧🇦 Bosnia and Herzegovina – Remote 🇨🇿 Czechia – Remote 🇭🇷 Croatia – Remote 🇲🇩 Moldova – Remote 🇧🇬 Bulgaria – Remote 🇲🇪 Montenegro – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 10%

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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 • 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 where 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 • 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 and improve failure modes • 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 • 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 • Production experience shipping LLM applications and agentic workflows • 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 • Experience building or owning an evaluation suite for a non-deterministic system • 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 • Willingness to collaborate across distributed, multicultural teams

🏖️ 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

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