PAY: $75-85/hour W2. Our company offers our consultants a suite of benefits after a qualification period including health, vision, dental, life and disability insurance.
100% remote role, candidates should be in CT or ET.
W2 candidates only, C2C not possible.
Description
As Senior machine learning engineer, you will play a pivotal role in the development, deployment and ongoing improvement of state-of-the-art machine learning models and systems to solve complex business challenges. The vision of the department is to empower technologists to accelerate business value delivery by building production-grade, packaged solutions that are secure, scalable, and compliant with Firm standards and policies.
The day to day:
- Build end-to-end Client solutions - from data extraction and cleaning to model development, evaluation, and deployment
- Research and prototype different modeling techniques to solve for accuracy, explainability, and other objectives
- Continuously monitor and improve model performance in production environments
- Develop frameworks, tools, and standards to enable scalable and reproducible machine learning development
- Collaborate with team members to resolve technical and code related issues.
- Participate in agile standard practice, ceremonies and demos
Basic qualification:
- 3+ years of experience building, deploying, and managing machine learning models in production environments
- 2+ years of experience with cloud platforms such as AWS, GCP, or Azure
- Strong programming skills in languages such as Python or Java.
- Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras.
- Experience of Client deployment practices including Experiment tracking, model versioning, monitoring, CI/CD using GitHub & terraform etc.
- Ability to communicate complex technical concepts clearly and effectively
- Strong problem-solving skills and analytical capabilities
Preferred:
- Familiarity with networking, Gen-AI, LLM training, and other cutting-edge techniques
- Experience in the security domain
- Knowledge of model observability
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Last updated on Oct 25, 2023