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Senior ML Engineer (GenAI, AWS)

3 hours, 21 minutes ago
Colombia
Full-time
Senior
Machine Learning Engineer
Software Development
Apache Spark AWS CI/CD CloudFormation Docker GCP Generative AI Machine Learning MLflow MLOps NumPy Pandas Python PyTorch SQL TensorFlow Terraform Transformers
Apply Now
Provectus

Provectus

Provectus provides consulting services in artificial intelligence and machine learning, assisting businesses in integrating AI solutions to meet their unique objectives and enhance their operational capabilities across various industries.

Professional Services
251-1K
Founded 2010
View All Jobs 31

Description

  • Design and implement end-to-end machine learning solutions from experimentation to production.
  • Build scalable ML pipelines and supporting 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.
  • Collaborate with cross-functional teams, including DevOps, Data Engineering, and solution architects.
  • Contribute to internal ML practice development and reusable ML accelerators.
  • Stay current with machine learning research and emerging technologies.
  • Participate in technical discussions and architectural decisions.

Requirements

  • Strong knowledge of machine learning fundamentals, including supervised, unsupervised, and reinforcement learning.
  • Experience with feature engineering, model training, evaluation, hyperparameter tuning, and validation.
  • Experience with classical ML libraries and frameworks such as TensorFlow, PyTorch, or similar.
  • Understanding of deep learning architectures such as CNNs, RNNs, and Transformers.
  • Experience building production LLM-based applications.
  • Ability to design effective prompts and chain-of-thought strategies.
  • Experience building retrieval-augmented generation (RAG) architectures.
  • Familiarity with embedding models, vector search, and vector databases.
  • Experience with LLM evaluation metrics and techniques.
  • Advanced proficiency in Python for ML applications.
  • Expertise with pandas, numpy, and data processing libraries.
  • Ability to work with SQL and structured data.
  • Experience building ETL/ELT data pipelines.
  • Experience with Spark or similar distributed computing frameworks.
  • Experience deploying ML models to production environments.
  • Proficiency with Docker and container orchestration.
  • Understanding of CI/CD for machine learning workflows.
  • Experience with model monitoring and observability.
  • Familiarity with MLflow, Weights and Biases, or similar experiment tracking tools.
  • Strong experience with AWS ML services such as SageMaker and Lambda.
  • Advanced knowledge of GCP ML and data services.
  • Understanding of cloud-native ML architectures.
  • Experience with infrastructure as code tools such as Terraform or CloudFormation.
  • Practical experience with AWS stack services such as SageMaker, ECR, EMR, S3, and AWS Lambda is preferred.
  • Practical experience with deep learning models is preferred.
  • Experience with taxonomies or ontologies is a plus.
  • Experience orchestrating complex machine learning workflows is a plus.
  • Practical experience with Spark/Dask and Great Expectations is a plus.

Benefits

  • Long-term B2B collaboration.
  • Fully remote setup.
  • Budget for medical insurance.
  • Paid sick leave, vacation, and public holidays.
  • Continuous learning support, including unlimited AWS certification sponsorship.

Interested in this position?

Apply directly on the company website

Apply Now

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