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SeniorFull-timePythonAWSGCPDockerTerraformMachine LearningTensorFlowPyTorchCI/CDAI
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for an ML Engineer to join our team at Provectus. You will be responsible for designing, developing, and deploying production-grade machine learning solutions for our clients. This role involves working on complex ML problems, mentoring junior engineers, and contributing to building ML accelerators and best practices.
Key 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
- →Mentor junior and mid-level ML engineers
- →Conduct code reviews and provide constructive feedback
- →Collaborate with cross-functional teams (DevOps, Data Engineering, SAs)
- →Stay current with ML research and emerging technologies
- →Propose improvements to existing solutions and processes
Requirements
- ✓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.)
- ✓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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