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5-8 years of as a Data Scientist with deep expertise in GenAI, NLP/LLM technologies. The candidate should have hands-on experience ML model building as well as training and fine-tuning large language models from the ground up. This role requires a strong technical background in machine learning, deep learning, and practical experience with modern AI frameworks.

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.
- Own business impact of AI solutions, including adoption, accuracy, latency, and cost efficiency
- Translate ambiguous business problems into structured AI solution approaches and measurable outcomes




- Drive solution success metrics (e.g., productivity gains, automation %, decision accuracy)
- Engage directly with business and technical stakeholders to understand requirements, present solutions, and influence decision-making
- Communicate solution architecture and trade-offs clearly to both technical and non-technical audiences
- Contribute to client discussions, PoCs, and proposal development.
- Design scalable, modular, and production-grade AI systems (APIs, pipelines, orchestration layers)
- Define architecture patterns for LLM applications (RAG pipelines, agentic workflows, hybrid systems)
- Make trade-offs across latency, cost, accuracy, and maintainability
- Build reusable accelerators, frameworks, and components that can be leveraged across multiple use cases and clients
- Contribute to internal IP creation (assets, templates, reference architectures)
- Ensure reliability and robustness of LLM systems through evaluation frameworks, guardrails, and fallback strategies
- Design protected and responsible AI systems (hallucination mitigation, bias handling, governance)
- Optimize cost-performance trade-offs in large-scale deployments
- Identify when NOT to use LLMs and propose alternative approaches
- Contribute to code reviews, design reviews, and mentorship of junior team members




- Drive quality standards and best practices across projects
- Stay ahead of advancements in GenAI and proactively evaluate their applicability to business problems
- Contribute to internal knowledge sharing, training, and capability building.

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, Claude Opus 4.6, GPT-3.5 Turbo, GPT-4, etc.
- At least one agentic Generative AI framework: CrewAI, AutoGen, LangGraph, n8n, 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).
- Databricks ecosystem: Databricks Genie, Databricks AI/BI, AgentBricks
- MS Copilot Studio and knowledge on no-code/low-code app development.
- MCP server, tools, skills and creation and maintenance of reusable componets.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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📌 GenAI-Engineer (Bengaluru)
🏢 Fractal Analytics
📍 Bengaluru