AI Data Scientist

About the Role

We are looking for a Data Scientist who is highly skilled in Python, Large Language Models (LLMs), Orchestrator Frameworks, and Multi-Agent Systems to build scalable, enterprise-grade AI solutions. The ideal candidate is hands-on, can convert real-world business problems into AI-driven workflows, and is comfortable working across cloud platforms, especially Microsoft Azure.

Key Responsibilities

• Build, fine-tune, and optimize LLM-based applications using open-source and proprietary models.
• Implement prompt engineering, retrieval-augmented generation (RAG), and evaluation pipelines.
• Develop multi-agentic systems with logical coordination, communication flows, and tool integrations.

• Design AI workflows using LangChain, LlamaIndex, CrewAI, or equivalent orchestrator frameworks.
• Implement autonomous agents, tool-calling strategies, memory modules, and context management.
• Build scalable agent-to-agent communication pipelines for enterprise use cases.

• Build robust data ingestion, processing, and feature engineering workflows in Python.
• Perform applied ML research and experimentation for structured, unstructured & multimodal data.
• Create data-driven insights, predictive models, and performance reports.

• Write clean, modular, production-ready Python code with strong software engineering discipline.
• Use Git/GitHub for versioning, branching, reviews, CI/CD pipelines, and release management.
• Collaborate with cross-functional engineering teams for integration and deployment.

• Develop and deploy AI services on Azure App Service, Azure Kubernetes Service (AKS), Azure Functions, Azure OpenAI, and Azure Storage.
• Work with scalable architectures: microservices, serverless, containerized (Docker) applications.
• Ensure enterprise-grade security, compliance, logging, monitoring, and observability.

• Design agent-based solutions for automation, RAG, decision-making, and workflow execution.
• Implement multi-agent architectures that utilize planning, reasoning, tool usage, and memory.
• Integrate agents with enterprise apps, APIs, Databricks, ADLS, n8n, or custom AI tools.

• Work closely with product owners, architects, and business stakeholders.
• Translate business requirements into technical solutions and present AI-driven proposals.
• Document architecture, workflows, and model behaviors with clarity.