Senior ML Engineer

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.

- 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

- 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.)

- Cloud Architecture: Understanding of cloud-native ML architectures

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