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.
