Machine Learning Engineer

About Impetus

Impetus Technologies is a digital engineering company focused on delivering expert services and products to help enterprises achieve their transformation goals. We solve the analytics, AI, and cloud puzzle, enabling businesses to drive unmatched innovation and growth.

Founded in 1991, we are cloud and data engineering leaders providing solutions to fortune 100 enterprises, headquartered in Los Gatos, California, with development centers in NOIDA, Indore, Gurugram, Bengaluru, Pune, and Hyderabad with over 3000 global team members. We also have offices in Canada and Australia and collaborate with a number of established companies, including American Express, Bank of America, Capital One, Toyota, United Airlines, and Verizon.

Experience- 4+ years

Job Description

• Strong experience in Python
• 2+ years’ experience of working on feature/data pipelines using PySpark
• Understanding and experience around data science
• Exposure to AWS cloud services such as Sagemaker, Bedrock, Kendra etc.
• Experience with machine learning model lifecycle management tools, and an understanding of MLOps principles and best practice
• Experience with statistical models e.g., multinomial logistic regression
• Experience of technical architecture, design, deployment, and operational level knowledge
• Exploratory Data Analysis
• Knowledge around Model building, Hyperparameter tuning and Model performance metrics.
• Statistics Knowledge (Probability Distributions, Hypothesis Testing)
• Time series modelling, Forecasting, Image/Video Analytics, and Natural Language Processing (NLP)

Good To Have:

• Experience researching and applying large language and Generative AI models.
• Experience with LangChain, LLAMAIndex, Foundation model tuning, Data Augmentation, and Performance Evaluation frameworks
• Able to provide analytical expertise in the process of model development, refining, and implementation in a variety of analytics problems.
• Knowledge on Docker and Kubernetes

Roles & Responsibilities

• Generate actionable insights for business improvements.
• Ability to understand business requirements.
• Write clean, efficient, and reusable code following best practices.
• Troubleshoot and debug applications to ensure optimal performance.
• Write unit test cases
• Collaborate with cross-functional teams to define and deliver new features
• Use case derivation and solution creation from structured/unstructured data.
• Actively drive a culture of knowledge-building and sharing within the team
• Experience applying theoretical models in an applied environment.
• MLOps, Data Pipeline, Data engineering
• Statistics Knowledge (Probability Distributions, Hypothesis Testing

Qualification

Degree – Graduates/Postgraduate in CSE/IT or related field