Senior ML Engineer – GenAI
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Job Description
Senior ML Engineer designing and deploying machine learning solutions for clients at Provectus. Collaborate with teams and mentor junior engineers while tackling complex ML problems.
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)
- 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
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