Senior ML Engineer, GenAI, AWS

This is a fully remote position, open to applicants in Colombia.

📋 Description

• Design and deploy comprehensive ML solutions, transitioning from experimentation to full production;

• Develop scalable ML pipelines and robust infrastructure;

• Enhance model performance, efficiency, and dependability;

• Produce clean, maintainable code suitable for production;

• Perform thorough experimentation and assessment of models;

• Diagnose and resolve intricate technical issues.

• Guide junior and mid-level ML engineers in their development;

• Conduct code reviews and offer constructive insights;

• Disseminate knowledge through documentation, presentations, and workshops;

• Work collaboratively with cross-functional teams, including DevOps, Data Engineering, and SAs;

• Play a role in the evolution of internal ML practices.

• Remain updated on ML research and the latest technologies;

• Suggest enhancements to current solutions and workflows;

• Assist in creating reusable ML accelerators;

• Engage in technical discussions and make architectural decisions.

⛳️ Requirements

• ML Fundamentals: knowledge of supervised, unsupervised, and reinforcement learning techniques;

• Model Development: expertise in feature engineering, model training, evaluation, hyperparameter tuning, and validation;

• ML Frameworks: proficiency with classical ML libraries, TensorFlow, PyTorch, or equivalent frameworks;

• Deep Learning: familiarity with CNNs, RNNs, and Transformers.

• LLM Applications: experience in developing production-level LLM-based applications;

• Prompt Engineering: capability to create effective prompts and chain-of-thought strategies;

• RAG Systems: background in constructing retrieval-augmented generation architectures;

• Vector Databases: understanding of embedding models and vector search methodologies;

• LLM Evaluation: proficiency in evaluation metrics and techniques for LLM outputs.

• Python: advanced skills in Python for ML applications;

• Data Manipulation: expert knowledge of pandas, numpy, and related data processing libraries;

• SQL: competence in working with structured data and databases;

• Data Pipelines: experience in developing ETL/ELT pipelines - Big Data: familiarity with Spark or similar distributed computing frameworks.

• Model Deployment: experience in deploying ML models to production settings;

• Containerization: expertise in Docker and container orchestration;

• CI/CD: understanding of continuous integration and deployment processes for ML;

• Monitoring: experience with model observation and monitoring;

• Experiment Tracking: familiarity with MLflow, Weights and Biases, or similar tools.

• AWS Services: extensive experience with AWS ML services (SageMaker, Lambda, etc.);

• GCP Expertise: advanced understanding of GCP ML and data services;

• Cloud Architecture: knowledge of cloud-native ML architectures;

• Infrastructure as Code: experience with Terraform, CloudFormation, or similar tools.

🏝️ Benefits

• Long-term B2B partnership

• Fully remote working environment

• Budget allocated for medical insurance

• Paid sick leave, vacation time, and public holidays

• Ongoing learning support, including unlimited sponsorship for AWS certifications.