Senior Machine Learning Engineer

Founded in the year 2017, CoffeeBeans specializes in offering high end consulting services in technology, product, and processes. We help our clients attain significant improvement in quality of delivery through impactful product launches, process simplification, and help build competencies that drive business outcomes across industries. The company uses new-age technologies to help its clients build superior products and realize better customer value. We also offer data-driven solutions and AI-based products for businesses operating in a wide range of product categories and service domains. As part of our continued platform evolution and expansion into generative AI, we are looking for a skilled Machine Learning Engineer to join our team. You will be responsible for designing, building, and deploying machine learning models, with a focus on GenAI, Retrieval-Augmented Generation (RAG), large language models (LLMs), and intelligent chatbot integration. Your work will enable rapid development of innovative use cases contextualized to our client's domain, significantly reducing time-to-market and enhancing end-user experience. Key Responsibilities: Design and develop machine learning and generative AI models to enhance platform capabilities, enable intelligent interactions, and support diverse use cases. Implement Retrieval-Augmented Generation (RAG) pipelines for contextual responses based on enterprise data. Integrate LLM-based chatbots with client-specific domain knowledge and data systems to ensure relevance and accuracy. Collaborate with Data Scientists, ML Engineers, and MLOps teams to operationalize models using Azure ML and other cloud-native services. Build and maintain scalable machine learning and RAG pipelines using Azure technologies. Fine-tune pre-trained LLMs and optimize model performance based on business and technical feedback. Continuously evaluate emerging techniques in generative AI, ML, and data science to recommend improvements and innovations. Work in an Agile environment and participate in team ceremonies and stakeholder interactions to align model development with business needs. Required Skills and Experience: 4–9 years of relevant experience in AI/Machine Learning, Data Science, or a related field. Strong proficiency in Python and experience with ML libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, etc. Hands-on experience with Azure ML, Azure Cognitive Services, Azure OpenAI, and related Azure data services (e.g., Azure Data Lake, Azure Functions, Azure Search). Experience building and deploying RAG architectures and working with LLMs for enterprise use cases. Understanding of vector databases (e.g., Pinecone, Weaviate, Azure AI Search with Vector Capabilities). Familiarity with chatbot development frameworks and integrating with front-end interfaces. Solid grasp of ML model lifecycle including versioning, monitoring, CI/CD, and MLOps principles. Experience working in collaborative, agile, and fast-paced environments. Databricks Desirable Skills: Familiarity with large-scale data processing using Spark, or similar. Knowledge of containerization and orchestration tools like Docker and Kubernetes. Exposure to domain-specific fine-tuning of LLMs and prompt engineering practices. Strong communication and stakeholder collaboration skills to understand client context and explain technical approaches effectively. Must Have Requirements Strong proficiency in Python and experience with ML libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, etc. BFSI Domain Experience