ML Engineer

Position: ML Engineer/Data Scientist

Notice Period: Immediate Joiner

Experience: 5+ Years

Key Responsibilities

Collect, clean, and preprocess data from diverse sources to ensure quality and

accuracy.

Develop and refine statistical models and Machine Learning (ML) algorithms to solve real-world problems.

Collaborate with cross-functional teams (e.g., data engineering, product, and business) to identify and understand analytical needs.

Perform exploratory data analysis (EDA) to uncover patterns, trends, and relationships in datasets.

Evaluate model performance using appropriate metrics, and iterate to optimize accuracy and efficiency.

Document analyses, methodologies, and best practices to maintain clear records for

future reference.

Stay up-to-date with emerging AI/ML technologies and actively explore new

approaches—especially in the areas of deep learning, Generative AI, and Large

Language Models (LLMs).

Present findings and insights to both technical and non-technical stakeholders in a clear, concise manner.

Qualifications

Design and Build Data Pipelines: Assist in the development of reliable, efficient

ETL (Extract, Transform, Load) and/or ELT pipelines that move data from a variety

of internal and external sources into data storage systems.

Data Integration and Collaboration: Work closely with data scientists, data

analysts, and software engineers to ensure data is readily available and properly

structured for analysis and modeling.

Data Quality and Validation: Implement quality checks, error handling, and

monitoring processes to ensure the reliability and accuracy of data.

Optimize Performance: Contribute to the optimization of data pipelines and

database queries to handle high-volume data and achieve better performance and

scalability.

Maintain Documentation: Develop and maintain technical documentation for data

systems, pipelines, and processes to keep the team aligned and informed.

Security and Compliance: Support efforts to ensure secure data handling and

compliance with relevant regulations and industry best practices.

Continuous Learning and Innovation: Stay informed about emerging data

engineering tools, techniques, and technologies; share learnings with the team; and

implement improvements where applicable

Skills: llm,algorithms,machine learning,ai/ml,python,sql,data scientist,deep learning,eda,generative ai,ml,genai