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Responsible for contributing to the development and deployment of machine learning algorithms. Evaluates accuracy and functionality of machine learning algorithms as a part of a larger team. Contributes to translating application requirements into machine learning problem statements. Analyzes and evaluates solutions both internally generated as well as third party supplied. Contributes to developing ways to use machine learning to solve problems and discover new products, working on a portion of the problem and collaborating with more senior researchers as needed. Works with moderate guidance in own area of knowledge.
Job Responsibility:
Contributes to the implementation, refinement and validation of machine learning algorithms for products and applications
Trains machine learning models, validates the accuracy of the machine learning models once trained and deploys validated machine learning models into production
Contributes to designs and development of data pipelines consisting of data ingest, data validation, data cleaning and data monitoring
Researches, writes and edits documentation and technical requirements, including evaluation plans, confluence pages, white papers, presentations, test results, technical manuals, formal recommendations and reports
Contributes to the company by creating patents, Application Programming Interfaces (APIs) and other intellectual property
Contributes to the testing and evaluation of solutions presented to the Company by various internal and external partners and vendors
Contributes to case studies, testing and reporting
Designs proof of concept solutions and contributes to studies to support future product or application development
Consistent exercise of independent judgment and discretion in matters of significance
Regular, consistent and punctual attendance
Must be able to work nights and weekends, variable schedule(s) and overtime as necessary
Other duties and responsibilities as assigned
Requirements:
Bachelor’s degree in Computer Science, Engineering, or related field
3–4 years of hands-on experience in ML engineering, ML testing, or applied ML roles
Strong understanding of ML fundamentals, debugging, and evaluation methodologies
Strong proficiency in Python
Hands-on experience with ML frameworks (scikit-learn, PyTorch, TensorFlow)
Experience building or extending testing frameworks (pytest, unittest)
Solid skills in Pandas, NumPy, and data analysis
Experience or exposure to LLMs and GenAI concepts: Prompt engineering, LLM evaluation techniques, RAG pipelines (basic understanding)
Familiarity with ML pipelines, model deployment workflows, and CI/CD
Curious mindset with interest in GenAI and LLM-driven automation
Detail-oriented, analytical, and comfortable working with complex systems
Nice to have:
Exposure to model monitoring, drift detection, and performance metrics is a plus