Senior Machine Learning Engineer

Job Description – Senior Engineer, Data Science & Engineering (5–8 Years)

Role Overview

We are looking for a highly skilled Senior Engineer, Data Science & Engineering with 5–8 years of experience in Data Engineering, Data Science, and Artificial Intelligence. The ideal candidate will have strong expertise in designing scalable data platforms, building AI/ML solutions, and implementing cloud-native architectures on AWS. You will work closely with business stakeholders and engineering teams to deliver data-driven solutions that power analytics and intelligent applications.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using modern data engineering frameworks.
• Build and optimize ETL/ELT workflows for batch and real-time data processing.
• Build AI/ML and Generative AI applications using modern LLM frameworks.
• Develop production-grade machine learning pipelines for model training, deployment, and monitoring.
• Design and implement data models for reporting, analytics, and AI workloads.
• Implement data quality, governance, observability, and monitoring frameworks.
• Integrate data from multiple internal and external sources using APIs, Kafka, and CDC pipelines.
• Collaborate with cross-functional teams to understand business requirements and translate them into scalable technical solutions.
• Leverage statistical methods, experimentation, and machine learning to analyze business data, validate hypotheses, and deliver predictive and prescriptive insights.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 5–8 years of experience in Data Engineering, Data Science & AI.
• Strong programming skills in Python and SQL.
• Hands-on experience with Apache Spark and distributed data processing.
• Strong experience with AWS services such as S3, Glue, EMR, Lambda, Athena, Redshift, IAM, CloudWatch, Step Functions, ECS/EKS, and DynamoDB.
• Hands-on experience with Spark ETL, Apache Kafka and Change Data Capture (CDC) pipelines.
• Strong understanding of supervised and unsupervised learning techniques, Classification, Regression, Clustering, Dimensionality Reduction Ensemble methods (Random Forest, XGBoost, LightGBM, CatBoost)
• Probability and statistics, Hypothesis testing and confidence intervals, Experimental design and A/B testing
• Experience with Generative AI, LLMs, prompt engineering, RAG architectures, vector databases, and AI orchestration frameworks such as LangChain or LlamaIndex.
• Strong analytical, problem-solving, and communication skills.
• Experience with Git, Docker, and CI/CD pipelines.