Senior ML Engineer (GenAI)

Provectus is a company focused on delivering machine learning solutions, and they are seeking a Senior ML Engineer to design, develop, and deploy production-grade machine learning systems. The role involves mentoring junior engineers, optimizing ML model performance, and collaborating with cross-functional teams to build ML accelerators and best practices.

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
• Infrastructure as Code: Experience with Terraform, CloudFormation, or similar
• 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