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Senior ML Engineer (GenAI)

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Provectus

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Location:
Colombia , Medellín ▼

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

As a Senior ML Engineer at Provectus, you'll be responsible for designing, developing, and deploying production-grade machine learning solutions for our clients. You will work on complex ML problems, mentor junior engineers, and contribute to building ML accelerators and best practices.

Job Responsibility:

  • 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
  • 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
  • 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

Requirements:

  • 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
  • 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
  • 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
  • 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
  • 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

Nice to have:

  • 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

Additional Information:

Job Posted:
January 10, 2026

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