About the role
Provectus
Responsibilities:
- 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;
Will be a plus:
What We Offer:
Interview stages:
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