Senior Analyst - AI/ML

What you'll do:

We are looking for a motivated and hands-on AI/ML Engineer with 4-5 years of experience to join our Artificial Intelligence and Machine Learning (AIML) team. The ideal candidate possesses strong foundations in machine learning, statistics, and software engineering, along with exposure to modern data platforms and MLOps practices.

In this role, you will collaborate with AI/ML engineers, data scientists, data engineers, and platform teams to design, develop, and operate machine learning solutions on enterprise-scale data ecosystems. You will contribute across the ML lifecycle, including data preparation, feature engineering, model development, experimentation, deployment, monitoring, and continuous improvement.

The position offers opportunities to work with large-scale datasets, cloud technologies, data engineering workflows, and AI/ML platforms while delivering impactful solutions to real-world business challenges.

Key Responsibilities

• Assist in building and training machine learning models for structured and unstructured data use cases
• Perform data analysis, preprocessing, and feature engineering on large datasets
• Support experimentation using AutoML and custom ML approaches
• Evaluate model performance and assist in tuning for accuracy and robustness
• Work with AI/ML platforms and tools for model development and experimentation
• Collaborate with engineers and analysts to understand business problems and translate them into ML tasks
• Document experiments, learnings, and model outcomes clearly
• Follow best practices for responsible AI, data governance, and security

• Collaborate with Data Engineering teams to build and maintain scalable data pipelines for ML training and inference workloads.

• Support data ingestion, transformation, and feature preparation workflows on enterprise data platforms such as Snowflake.

• Assist in operationalizing machine learning models through CI/CD pipelines and MLOps best practices.

• Participate in monitoring, testing, and maintaining AI/ML solutions in development and production environments.

• Work with cloud and DevOps teams to automate model deployment, versioning, and reproducible experimentation workflows.

• Artificial Intelligence / Machine Learning / Data Science

• Data Engineering Fundamentals

• MLOps and ML Lifecycle Management

• Python,SQL,Data pipelines and ETL/ELT concepts

• Cloud platforms (Azure / AWS / GCP)

• Data platforms like Snowflake

• Data Engineering concepts including ETL/ELT pipelines and data warehousing

• DevOps and MLOps practices for ML deployment and monitoring

• CI/CD tools such as Azure DevOps or GitHub Actions,Docker and containerized ML workloads,ML lifecycle concepts (training, evaluation, deployment, monitoring.

• GIT Version Control,Model Versioning and Experiment Tracking,Deployment and Monitoring of ML Models

• Data Modelling and Large Scale Dataset Processing,Data Quality and Governance Principles