[Hiring] Senior ML Engineer @Provectus

Role Description

As an ML Engineer, you’ll be provided with all opportunities for development and growth. Let's work together to build a better future for everyone!

Responsibilities:

• Technical Delivery (60%):
  • 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.
• Collaboration and Contribution (25%):
  • 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.
• Innovation and Growth (15%):
  • 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.

Qualifications

• 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.

Requirements

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda);
• Practical experience with deep learning models;
• Experience with taxonomies or ontologies;
• Practical experience with machine learning pipelines to orchestrate complicated workflows;
• Practical experience with Spark/Dask, Great Expectations.

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.

Interview Stages

• Recruitment Interview;
• Tech interview;
• HR Interview;
• HM Interview.