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Senior ML Engineer (GenAI, AWS)

1 week, 4 days ago
Colombia
Full-time
Senior
Machine Learning Engineer
Software Development
Apache Spark AWS CI/CD CloudFormation Docker Feature Engineering GCP Generative AI Machine Learning MLflow MLOps NumPy Pandas Python PyTorch Reinforcement Learning SageMaker 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 35

Description

  • Design and implement end-to-end ML solutions from experimentation through 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.
  • Perform 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 accelerators.
  • Stay current with ML 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 or PyTorch.
  • Knowledge 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) systems.
  • Familiarity with embedding models, vector search, and vector databases.
  • Experience with LLM evaluation metrics and techniques.
  • Advanced proficiency in Python for ML applications and expert-level use of pandas and numpy.
  • Ability to work with SQL and structured data.
  • Experience building ETL/ELT data pipelines and working 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 ML and experience with model monitoring and observability.
  • Familiarity with experiment tracking tools such as MLflow or Weights & Biases.
  • 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.
  • Experience with AWS stack services such as ECR, EMR, and S3 is preferred.
  • Practical experience with deep learning models is a plus.
  • Experience with taxonomies or ontologies is a plus.
  • Experience orchestrating complex machine learning workflows is a plus.
  • Experience with Spark, Dask, or Great Expectations is a plus.

Benefits

  • Long-term B2B collaboration.
  • Fully remote work setup.
  • Medical insurance budget.
  • Paid sick leave, vacation, and public holidays.
  • Continuous learning support.
  • Unlimited AWS certification sponsorship.

Interested in this position?

Apply directly on the company website

Apply Now

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