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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.