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.
Core Responsibilities
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
Requirements
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
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.
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📌 Senior Ml Engineer (Genai) (Medellín)
🏢 Provectus
📍 Medellín