Description

About the Role

Join a high-impact AI and Data Engineering team building scalable, cloud-native machine learning solutions that power enterprise analytics, intelligent automation, and next-generation AI applications. You'll work on designing and deploying production-grade ML systems, implementing MLOps best practices, and developing cutting-edge Generative AI solutions for enterprise customers.

This is an opportunity to work on real-world AI challenges, leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and cloud-native data platforms to drive business innovation.

Key Responsibilities

• Design, develop, and deploy scalable machine learning solutions for enterprise customers.
• Build and productionize ML workloads using MLOps best practices across multiple business domains.
• Develop Generative AI applications using Large Language Models (LLMs), including:
• Retrieval-Augmented Generation (RAG) solutions on enterprise knowledge repositories.
• Natural language querying over structured and unstructured data.
• AI-powered content generation and intelligent assistants.
• Collaborate with data engineering and business teams to design robust AI and ML architectures.
• Build, monitor, and optimize production ML pipelines, including model performance and drift monitoring.
• Provide technical guidance on machine learning architecture, tooling, and industry best practices.
• Work with large-scale distributed data processing platforms to build efficient and scalable ML solutions.

Required Skills & Qualifications

• 4–6 years of experience for Senior Machine Learning Engineer or 6+ years for ML Architect.
• Strong programming experience in Python.
• Hands-on experience with machine learning libraries such as:
• Pandas
• Scikit-learn
• MLflow
• TensorFlow and/or PyTorch
• Gensim
• NLTK
• Experience deploying and managing production-grade machine learning systems.
• Strong understanding of MLOps, including model deployment, monitoring, CI/CD, and drift detection.
• Experience working with at least one major cloud platform:
• Microsoft Azure (preferred)
• AWS
• Google Cloud Platform (GCP)
• Experience building LLM-powered applications using RAG architectures and enterprise data sources.
• Strong understanding of machine learning lifecycle, model optimization, and production deployment.
• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Mathematics, Operations Research, or a related quantitative discipline.

Preferred Qualifications

• Experience with Apache Spark for large-scale distributed data processing.
• Hands-on experience with the Databricks platform.
• Experience with Azure Machine Learning or similar cloud AI services.
• Familiarity with vector databases and modern LLM orchestration frameworks such as LangChain or LlamaIndex.
• Experience designing scalable AI/ML architectures for enterprise applications.

What We're Looking For

We're looking for engineers who have successfully built and deployed production ML systems—not just trained models. The ideal candidate has hands-on experience with MLOps, cloud-native AI solutions, and enterprise-scale LLM applications, with a strong focus on delivering business impact through scalable machine learning systems

Key Skills

Education

Any Graduate