Machine Learning Engineer

Req. Title

Ml Engineer

Location

Remote ; Monthly 5 days come to office

Client : Impetus

Payroll : Thinqor solutions

Experience : 4-6 years

Compenation : Up to 40% hike on CTC based interview performance

Description

• Strong experience in Python 
• 3+ 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). 

Role

• 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) 

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.