Senior AI Engineer – GenAI & Agentic Systems

Provectus
Spain
On-site

Who this role is best for

Best suited to senior AI engineers with production-grade LLM experience who thrive in cloud-based ML workflows, particularly on AWS.

Best fit for

  • Senior engineers who have deployed LLMs in production environments.
    — “Strong hands-on experience with LLMs in production”
  • Candidates comfortable with AWS infrastructure for ML deployments.
    — “AWS Bedrock experience strongly preferred”
  • Developers who prioritize modular, production-ready code over notebook scripts.
    — “ability to produce well-structured modules, not only notebook scripts”

Things to consider

  • Expectation to maintain models in production post-deployment.
    — “Monitor and maintain ML models in production”
  • Documentation skills are required, not just technical implementation.
    — “Write clear and comprehensive documentation for ML models”

How to stand out

  • Highlight specific RAG architecture implementations you've designed.
    — “RAG architecture, and agentic systems”
  • Quantify performance improvements from your production model optimizations.
    — “improve existing models”
  • Demonstrate AWS cloud integration in your ML pipeline examples.
    — “Practical experience with cloud platforms (AWS stack is preferred”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • creation and improvement of ML models
  • collaboration with engineering and product teams
Typical background
strong hands-on experience with LLMs and agentic systems

Skills & requirements

Required

ML AlgorithmsLlmsAgentic SystemsAWS BedrockFeature EngineeringModel Production

Preferred

Cloud PlatformsDeep LearningTaxonomiesMachine Learning PipelinesSpark/daskGreat Expectations

Stack & domain

LlmsRag ArchitectureAgentic SystemsAws BedrockClassificationRegressionFeature EngineeringMl ModelsProductionNLPRecommendation EnginesSoftware EngineeringPythonDockerCloud PlatformsDeep LearningTaxonomiesOntologiesMachine Learning PipelinesSparkDaskGreat ExpectationsCommunicationProblem-solvingTeamworkAdaptabilityCuriosityContinuous LearningAIMLCloud ServicesData Engineering

About the role

Original posting from Provectus via Lever

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Let's work together to build a better future for everyone!

Requirements::

  • Comfortable with standard ML algorithms and underlying math.
  • Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
  • AWS Bedrock experience strongly preferred
  • Practical experience with solving classification and regression tasks in general, feature engineering.
  • Practical experience with ML models in production.
  • Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
  • Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
  • Python expertise, Docker.
  • English level - strong upper- intermediate.
  • Excellent communication and problem-solving skills.

Will be a plus::

  • Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
  • Practical experience with deep learning models.
  • Experience with taxonomies or ontologies.
  • Practical experience with machine learning pipelines to orchestrate complicated workflows.
  • Practical experience with Spark/Dask, Great Expectations.

Responsibilities::

  • Create ML models from scratch or improve existing models. 
  • Collaborate with the engineering team, data scientists, and product managers on production models.
  • Develop experimentation roadmap. 
  • Set up a reproducible experimentation environment and maintain experimentation pipelines.
  • Monitor and maintain ML models in production to ensure optimal performance.
  • Write clear and comprehensive documentation for ML models, processes, and pipelines.
  • Stay updated with the latest developments in ML and AI and propose innovative solutions.

Source: Provectus careers (Lever)

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