Machine Learning Engineer(Databricks+MlOps

Senior Machine Learning Engineer / ML Architect
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.

Work arrangement
No