As a Data Scientist, you will identify business trends and solve complex problems using large-scale data and advanced AI techniques. You will design, develop, and deploy high-impact solutions ranging from classical ML/DL to LLM-powered applications including RAG-based architectures and Agentic AI systems.

Key Responsibilities :

- Analyze existing digital products to understand current intelligent models and improve their performance, reliability, and scalability.

- Enhance traditional ML and DL pipelines by incorporating LLM-based capabilities such as summarization, Q&A, reasoning, decision support, and copilots.

- Design and implement LLM-based solutions using Retrieval-Augmented Generation (RAG) to ground responses on enterprise data.

- Build Agentic AI workflows that enable multi-step task planning, tool and API invocation, and contextual memory management.

- Develop agent orchestration patterns such as multi-agent collaboration, deterministic workflow engines, and fallback mechanisms.

- Drive innovation through experimentation and contribute to invention disclosures and patents.

- Design and implement AI solutions for IoT, robotics, and automation use cases.

- Build and maintain scalable pipelines for model training, evaluation, and deployment.

- Manage experiment tracking, model versioning, and model registries.

- Define and track LLM-specific evaluation metrics including groundedness, faithfulness, hallucination rate, and safety.

- Monitor retrieval system quality using metrics such as precision, recall, and latency.

Required Qualifications :

- Masters degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related field (PhD preferred).

- Strong oral and written communication skills.

- Demonstrated ability to take ambiguous objectives and design innovative, flexible solutions.

- Proven track record of delivering impactful outcomes.

Technical Skills :

- Expertise in Large Language Models (LLMs) and building production-grade applications.

- Hands-on experience with RAG architectures, embeddings, vector search, and reranking mechanisms.

- Experience building tool-using agents and multi-step agent workflows.

- Familiarity with vector databases (e.g., Pinecone, Milvus, Weaviate, Elasticsearch).

- Experience with agent frameworks (e.g., LangChain, Semantic Kernel, LlamaIndex).

- Experience with LLMOps tooling: prompt management, evaluation harnesses, and observability.

- Cloud experience (Azure/AWS/GCP), containerization (Docker), and Kubernetes.

Behavioral Competencies :

- Strong ownership mindset and proactive leadership.

- Collaborative team player with an innovation-first approach.