Provectus
Provectus1mo ago

Senior ML Engineer (GenAI)

MedellínRemoteFull-timesenior
OtherMachine Learning EngineerData
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Quick Summary

Overview

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations,

Technical Tools
OtherMachine Learning EngineerData
Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.

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!
  • Technical Delivery (60%)
  • - Design and implement end-to-end ML solutions from experimentation to production;
    - Build scalable ML pipelines and infrastructure;
    - Optimize model performance, efficiency, and reliability;
    - Write clean, maintainable, production-quality code;
    - Conduct rigorous experimentation and model evaluation;
    - Troubleshoot and resolve complex technical challenges.

  • Collaboration and Contribution (25%);
  • - Mentor junior and mid-level ML engineers;
    - Conduct code reviews and provide constructive feedback;
    - Share knowledge through documentation, presentations, and workshops;
    - Collaborate with cross-functional teams (DevOps, Data Engineering, SAs);
    - Contribute to internal ML practice development.

  • Innovation and Growth (15%)
  • - Stay current with ML research and emerging technologies;
    - Propose improvements to existing solutions and processes;
    - Contribute to the development of reusable ML accelerators;
    - Participate in technical discussions and architectural decisions.
  • Machine Learning Core
  • - ML Fundamentals: supervised, unsupervised, and reinforcement learning;
    - Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation;
    - ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks;
    - Deep Learning: CNNs, RNNs, Transformers.
  • LLMs and Generative AI
  • - LLM Applications: Experience building production LLM-based applications;
    - Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies;
    - RAG Systems: Experience building retrieval-augmented generation architectures;
    - Vector Databases: Familiarity with embedding models and vector search;
    - LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs.
  • Data and Programming
  • - Python: Advanced proficiency in Python for ML applications;
    - Data Manipulation: Expert with pandas, numpy, and data processing libraries;
    - SQL: Ability to work with structured data and databases;
    - Data Pipelines: Experience building ETL/ELT pipelines - Big Data: Experience with Spark or similar distributed computing frameworks.
  • MLOps and Production
  • - Model Deployment: Experience deploying ML models to production environments;
    - Containerization: Proficiency with Docker and container orchestration;
    - CI/CD: Understanding of continuous integration and deployment for ML;
    - Monitoring: Experience with model monitoring and observability;
    - Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools.
  • Cloud and Infrastructure
  • - AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.);
    -GCP Expertise: Advanced knowledge of GCP ML and data services;
    - Cloud Architecture: Understanding of cloud-native ML architectures;
    - Infrastructure as Code: Experience with Terraform, CloudFormation, or similar.
  • 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.
  • Long-term B2B collaboration;
  • Fully remote setup;
  • A budget for your medical insurance;
  • Paid sick leave, vacation, public holidays;
  • Continuous learning support, including unlimited AWS certification sponsorship.
  • Recruitment Interview;
  • Tech interview;
  • HR Interview;
  • HM Interview.
  • Listing Details

    Posted
    March 6, 2026
    First seen
    March 26, 2026
    Last seen
    April 24, 2026

    Posting Health

    Days active
    28
    Repost count
    0
    Trust Level
    38%
    Scored at
    April 24, 2026

    Signal breakdown

    freshnesssource trustcontent trustemployer trust
    Provectus

    Provectus is a leading AI consultancy established in 2010, focused on driving innovation through tailored AI solutions.

    Employees
    750
    Founded
    2010
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    ProvectusSenior ML Engineer (GenAI)