Senior ML Engineer (GenAI) at Provectus

As a Senior ML Engineer at Provectus, you'll be responsible for designing, developing, and deploying production-grade machine learning solutions for our clients. You will work on complex ML problems, mentor junior engineers, and contribute to building ML accelerators and best practices. Core Responsibilities: • 1. 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 • 2. 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 • 3. 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 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 Will be a plus: • 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.