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Provectus

Senior AI/ML Engineer, GenAI, AWS

Provectus

. Work in a pair with an FDE and an FDX .

Posted 10/30/2026full-timeRemote • North Macedonia, Bosnia & Herzegovina, Czechia, Croatia, Moldova, Bulgaria, MontenegroSeniorWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in building and optimizing production GenAI systems, with a strong foundation in AI/ML principles and hands-on experience in deploying applications on cloud platforms like AWS and GCP/Azure. Proficient in Python and/or TypeScript, with a focus on integrating AI components into backend services and ensuring model performance and reliability.

Highest-signal resume keywords
Production Experience Shipping LLM ApplicationsHands-On AWS Production ExperienceSolid AI/ML FoundationsExperience with LLM APIsStrong Engineering Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Python ProficiencyTypeScript ProficiencyAI/ML FoundationsProduction Code WritingRAG Systems OptimizationModel EvaluationCost and Latency OptimizationMulti-Step WorkflowsGraph-Based OrchestrationAgent Monitoring
Soft Skills
Excellent Communication SkillsProblem-Solving SkillsProactive and Self-DirectedComfort with AmbiguityOwnership
Tools & Technologies
AWS BedrockOpenAIECSKubernetesCI/CDContainersLambdaS3SQSECR
Industry Keywords
GenAI SystemsLLMOpsAgentOpsCloud-Native DeliveryEvaluation SuiteModel Drift DetectionAgent FrameworksProduction SystemsNon-Deterministic SystemsMCP Knowledge

Tech Stack

Tools & technologies
AWSAzureCloudGoogle Cloud PlatformKubernetesPythonTypeScript

About the role

Key responsibilities & impact
  • 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

What you’ll need
  • 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

Comp & perks
  • 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