Job Description: Data Scientist (GenAI)
Experience: 3+ years
Notice Period- Immediate- 30days
Responsibilities
- Design, develop, and implement advanced solutions leveraging Large Language Models (LLMs).
- Take full ownership of initiatives, delivering end-to-end solutions with minimal supervision.
- Stay current with the latest advancements in Generative AI, LLMs, RAG systems, and applied research.
- Build and maintain reusable code libraries, tools, and frameworks to accelerate AI development.
- Participate in code reviews to ensure high-quality, maintainable, and scalable solutions.
- Contribute across the entire software development lifecycle—design, implementation, testing, deployment, and maintenance.
- Collaborate with cross-functional teams to align AI solutions with business goals, integrate contributions into core systems, and influence roadmaps.
- Apply strong analytical and problem-solving skills to design efficient solutions for complex business challenges.
- Communicate effectively across technical and non-technical teams, ensuring transparency and alignment.
Must-Have Skills
Generative AI & NLP
- SaaS-based LLMs: LangChain, LlamaIndex, vector databases, prompt engineering (CoT, ReAct, agents), Azure OpenAI function calling, multimodal models.
- Open-Source and SaaS LLMs: Azure OpenAI, GPT-3.5 Turbo, GPT-4, etc.
- At least one agentic Generative AI framework: CrewAI, AutoGen, LangGraph, LangFlow, SmolAgents, Semantic Kernel.
- Advanced Retrieval-Augmented Generation (RAG) systems: hybrid retrieval, knowledge graph–based retrieval, multi-hop RAG, hierarchical/contextual retrieval strategies, evaluation/monitoring of RAG pipelines.
- Classical NLP: text classification, topic modeling, Q&A systems, conversational AI/chatbots, search, Document AI, summarization, content generation, and Named Entity Recognition (NER).
Tech Stack
- Programming & Frameworks: Python, FastAPI
- Cloud & DevOps: Azure DevOps, Agile (Azure Boards)
- AI/ML Tools: Azure Databricks, MLFlow Model Lifecycle Management, Unity Catalog (Azure Databricks)
- Cloud Services: Azure Function Apps, Azure Blob Storage, Azure Cognitive Services, Azure AI Search
- Productivity Tools: Microsoft Copilot Studio (basic)
Good-to-Have Skills
Ops & Engineering
- AgentOps / LLMOps:
- Agent monitoring, evaluation, and debugging frameworks.
- LLM observability and tracing (LangSmith, LangFuse, Weights & Biases ).
- Prompt/version management and experimentation.
- Governance, compliance, and cost optimization for LLMs.
- CI/CD pipelines in Azure DevOps.
- Flask, Docker.
Other AI/ML Skills
- Document digitization and OCR methods.
- Azure Document Intelligence or equivalent.
- Azure Delta Lake.
Behavioral Competencies
- Flexible to contribute to ad-hoc initiatives such as PoCs, solution prototyping, and proposal workflows.
- Open to working on non-GenAI AI/ML projects (e.g., computer vision, document digitization, data structuring, brainstorming for business use cases).
- Proactive in providing timely updates and driving tasks to completion.
- Demonstrates responsibility, accountability, curiosity, and an innovative mindset.
- Willingness to learn and understand the business context (e.g., Philips domain and data landscape) beyond core technical skills.
