Data Scientist

Role Overview

We are seeking a highly skilled professional to analyze existing digital products and enhance traditional ML and DL pipelines by incorporating LLM-based capabilities. You will design and implement RAG architectures, build Agentic AI workflows, and develop agent orchestration patterns to drive innovation in IoT, robotics, and automation.

Responsibilities

• Analyze existing digital products to improve performance, reliability, and scalability of intelligent models.
• Enhance ML/DL pipelines with LLM capabilities like summarization, Q&A, and reasoning.
• Design and implement Retrieval-Augmented Generation (RAG) solutions grounded on enterprise data.
• Build Agentic AI workflows including multi-step task planning and tool/API invocation.
• Develop agent orchestration patterns such as multi-agent collaboration and deterministic workflow engines.
• Design AI solutions for IoT, robotics, and automation use cases.
• Build and maintain scalable pipelines for model training, evaluation, and deployment.
• Define and track LLM-specific evaluation metrics including groundedness and hallucination rates.

Requirements

• Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related field (PhD preferred).
• Strong expertise in Large Language Models (LLMs) and production-grade applications.
• Hands-on experience designing and implementing RAG architectures.
• Experience with embeddings, vector representations, and vector search techniques.
• Proven ability to design multi-step agent workflows with safe execution patterns.
• Strong communication skills and ability to handle ambiguous objectives.

Skills

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• LangChain
• Vector Databases
• Python

Nice to Have

• Experience with vector databases like Pinecone, Milvus, or Weaviate.
• Familiarity with LangChain, Semantic Kernel, or LlamaIndex.
• Experience with LLMOps, Docker, and Kubernetes.
• Cloud experience with Azure, AWS, or GCP.
• Background in Reinforcement Learning or optimization theory.