AI/ML Engineer

We are looking for an AI/ML Engineer with a strong inclination toward Generative AI and Large Language Models (LLMs) to build intelligent, scalable, and production-grade AI applications. You will work on real-world use cases involving NLP, GenAI, predictive modelling, and data-driven automation, closely collaborating with data engineering and product teams.

Responsibilities Design, develop, train, and deploy machine learning and Generative AI models for real-world business use cases including prediction, automation, NLP, and decision intelligence. Build and optimize LLM-powered applications such as chatbots, document intelligence systems, summarization engines, and RAG (Retrieval-Augmented Generation) pipelines with hands-on experience in LLMs, embeddings, vector search, and RAG architectures. Perform data preprocessing, feature engineering, model evaluation, and hyperparameter tuning to ensure accuracy, reliability, and scalability. Develop and manage end-to-end ML and GenAI pipelines, including training, validation, versioning, deployment, monitoring, and optimization. Work with Snowflake, Databricks and other cloud platforms (GCP, AWS, Azure) to enable scalable data-driven AI solutions. Monitor model performance, drift, cost, and latency, and apply continuous optimization to production AI systems. Collaborate with cross-functional teams to translate business problems into scalable AI solutions. Apply AI governance, security, and responsible AI practices across ML and LLM systems.

Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or related fields. 2–3 years of hands-on experience in Machine Learning / AI projects. Hands-on experience building Generative AI applications using Large Language Models (LLMs) . Experience with prompt engineering, retrieval-augmented generation (RAG), and integrating LLMs with structured and unstructured data sources. Experience leveraging enterprise GenAI platforms and frameworks (e.g., Snowflake Cortex, Databricks Mosaic AI, OpenAI-compatible APIs) to build scalable AI solutions. Strong programming skills in Python and ML libraries like XGBoost, Pytorch, Tensorflow. Strong understanding of Model Evaluation Techniques and Feature Engineering. Proficient with SQL and data handling. Strong experience with model deployment. Experience applying MLOps practices, including CI/CD for ML workflows and model versioning. Hands-on experience with LangChain, LlamaIndex, and LangGraph to orchestrate agents, workflows, and multi-step AI pipelines. Working experience with platforms like Snowflake, Databricks is a plus