Junior AI/ML Engineer, GenAI, AWS

Provectus

. Build and contribute to RAG system components under senior guidance, with growing autonomy .

Posted 10/9/2026full-timeRemote • Ukraine, Spain, Serbia, Armenia, PolandJuniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates proficiency in Python and TypeScript for building RAG systems and integrating AI components into backend services. Possesses hands-on experience with AWS services and CI/CD practices, along with a solid foundation in AI/ML principles and model evaluation.

Highest-signal resume keywords

Python ProficiencyTypeScript ProficiencyAWS ExperienceCI/CD ExperienceAI/ML Foundations

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

RAG Systems DevelopmentProduction Code WritingRESTful API IntegrationModel EvaluationTest WritingData Pipeline DevelopmentContainerizationEvaluation Harness BuildingLLM API ExperienceMonitoring Concepts

Soft Skills

Excellent CommunicationProblem-Solving SkillsProactive AttitudeSelf-DirectedComfort with Ambiguity

Tools & Technologies

AWS LambdaAWS S3AWS ECSCI/CD ToolsContainerization Tools

Industry Keywords

AI ComponentsBackend ServicesMulticultural CollaborationTechnical DiscussionsArchitectural Decisions

Tech Stack

Tools & technologies

AWSPythonTypeScript

About the role

Key responsibilities & impact

Build and contribute to RAG system components under senior guidance, with growing autonomy
Write tests and help build evaluation harnesses
Write production code across AI, backend services, and data pipelines
Integrate AI components into backend services and RESTful APIs
Support deployment of containerized systems to AWS using CI/CD
Contribute to documentation, runbooks, and client handover materials
Participate in technical discussions and architectural decisions
Support model evaluation and investigate and improve failure modes
Take increasing ownership of components and technical decisions

Requirements

What you’ll need

Proactive and self-directed; push 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
Python and/or TypeScript proficiency
Practical AWS experience, such as Lambda, S3, or ECS
Some experience with containers and CI/CD in real projects
Exposure to evaluating non-deterministic systems and running test/evaluation cycles
Basic working knowledge of model/agent monitoring concepts
Awareness of cost and latency trade-offs when working with LLMs
Practical experience with LLM APIs, including Anthropic, AWS Bedrock, or OpenAI
2+ years of software or ML engineering experience, including exposure to production systems
Solid AI/ML foundations and understanding of common model failure modes

Benefits

Comp & perks

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 tech needed for comfortable, productive work