{"additional": "", "additionalPlain": "", "categories": {"commitment": "Full-time", "department": "Engineering", "location": "Medellín, Antioquia", "team": "AI practice - Diego Martinez", "allLocations": ["Medellín, Antioquia", "Bogotá, Capital District", "Cali, Valle del Cauca", "Barranquilla", "Bucaramanga, Santander"]}, "createdAt": 1779720880122, "description": "", "descriptionPlain": "", "id": "76640225-4aa7-45a3-bcdc-cb156271057b", "lists": [{"text": "Responsibilities:", "content": "\n

Technical Delivery (60%)

\n\n

- 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.

\n\n

Collaboration and Contribution (25%);

\n\n

- 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.

\n\n

Innovation and Growth (15%)

\n\n

- 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.

"}, {"text": " Requirements:", "content": "\n

Machine Learning Core

\n\n

- 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.

\n\n

LLMs and Generative AI

\n\n

- 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.

\n\n

Data and Programming

\n\n

- 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.

\n\n

MLOps and Production

\n\n

- 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.

\n\n

Cloud and Infrastructure

\n\n

- 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;

\n\n

- Infrastructure as Code: Experience with Terraform, CloudFormation, or similar.

\n"}, {"text": "Will be a plus:", "content": "\n

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.

\n"}, {"text": "What We Offer:", "content": "\n

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.

\n"}, {"text": "Interview stages:", "content": "\n

Recruitment Interview;

Tech interview;

HR Interview;

HM Interview.

\n"}], "text": "Senior ML Engineer (GenAI, AWS)", "country": "CO", "workplaceType": "remote", "opening": "", "openingPlain": "", "descriptionBody": "", "descriptionBodyPlain": "", "hostedUrl": "https://jobs.lever.co/provectus/76640225-4aa7-45a3-bcdc-cb156271057b", "applyUrl": "https://jobs.lever.co/provectus/76640225-4aa7-45a3-bcdc-cb156271057b/apply"}

Senior ML Engineer (GenAI, AWS)

Who this role is best for

Skills & requirements

Stack & domain

Similar roles