Provectus

Senior ML Engineer – GenAI

Provectus

full-time

Posted on:

Location Type: Remote

Location: Remote • 🇨🇴 Colombia

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Job Level

Senior

Tech Stack

AWSCloudDockerETLGoogle Cloud PlatformNumpyPandasPythonPyTorchSparkSQLTensorflowTerraform

About the role

  • 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)
  • 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

  • 1. Machine Learning Core
  • - - 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
  • 2. LLMs and Generative AI
  • - - 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
  • 3. Data and Programming
  • - - 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
  • 4. MLOps and Production
  • - - 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
  • 5. Cloud and Infrastructure
  • - - 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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
Machine LearningML FundamentalsModel DevelopmentML FrameworksDeep LearningLLMsPrompt EngineeringData ManipulationPythonModel Deployment
Soft skills
MentoringCollaborationCommunicationProblem SolvingTechnical DiscussionFeedbackDocumentationPresentationExperimentationOptimization