Role Overview

We are looking for a hands-on professional with practical experience in developing and deploying real-world AI/ML and GenAI solutions, particularly LLM-based applications that have moved beyond the prototype stage and are production-ready. This delivery-focused role will involve working with high-scale data, production ML pipelines, and implementing GenAI-powered features across our analytics and intelligent applications.

Key Responsibilities

• Design, build, and deploy LLM-based and GenAI solutions for production use cases.
• Develop Retrieval-Augmented Generation (RAG) pipelines, LLM workflows, and AI-powered assistants.
• Perform prompt engineering, model evaluation, and fine-tuning workflows.
• Build and optimize advanced ML models for prediction and analytics.
• Work with large-scale structured and unstructured datasets.
• Develop scalable model inference and serving APIs.
• Integrate AI/ML systems with product and data platforms.
• Improve model performance, latency, and cost efficiency.
• Collaborate with engineering and product teams to productionize AI features.
• Continuously monitor and improve deployed AI systems.

Required Skills

• Strong Python programming and ML ecosystem experience.
• Hands-on experience building LLM and Generative AI applications.
• Experience with prompt engineering, RAG architectures, embeddings, and vector search.
• Experience with LLM tooling/frameworks such as LangChain, LlamaIndex, or similar.
• Experience with vector databases like FAISS, Milvus, Pinecone, or equivalent.
• Practical experience with PyTorch or TensorFlow.
• Experience working with large-scale datasets and distributed data processing.
• Experience deploying ML/AI models as APIs or production services.
• Strong SQL and data analysis skills.
• Understanding of ML model lifecycle, evaluation, and monitoring.
• Exposure to MLOps practices and experiment tracking.
• Experience with cloud platforms (AWS / GCP / Azure) is preferred.
• Familiarity with streaming or high-volume telemetry data systems is a plus.

Ideal Profile

• Has successfully shipped at least one LLM/GenAI solution in production or near-production environments.
• Strong problem-solver with a build-first mindset.
• Comfortable working with ambiguity and fast iteration cycles.
• Focused on practical, scalable AI solutions, not just experimental notebooks.
• Clear communicator capable of translating business needs into effective AI solutions.