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Senior ML Engineer – GenAI, AWS at Provectus | JobVerse
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Senior ML Engineer – GenAI, AWS
Provectus
Remote
Website
LinkedIn
Senior ML Engineer – GenAI, AWS
Colombia
Full Time
5 hours ago
Visa Sponsorship
Apply Now
Key skills
AWS
Cloud
Docker
ETL
Google Cloud Platform
Numpy
Pandas
Python
PyTorch
Spark
SQL
Tensorflow
Terraform
ML
Deep Learning
LLM
RAG
TensorFlow
NumPy
MLflow
ELT
Data Engineering
GCP
Google Cloud
CloudFormation
Lambda
SageMaker
CI/CD
Collaboration
About this role
Role Overview
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.
Tech Stack
AWS
Cloud
Docker
ETL
Google Cloud Platform
Numpy
Pandas
Python
PyTorch
Spark
SQL
Tensorflow
Terraform
Benefits
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.
Apply Now
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