GenAI-Engineer

Fractal Analytics; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Fractal Analytics in Bengaluru is seeking an experienced Data Scientist with 5-8 years of GenAI expertise to design and deploy advanced NLP and LLM solutions. The role requires hands-on ML model building, training, and tuning of large language models with a strong ML/DL background.

You willOwn end-to-end AI initiatives, collaborate with cross-functional teams, and continuously evaluate state-of-the-art GenAI techniques to deliver scalable, production-ready systems.

Qualifications

• 5-8 years of experience as a Data Scientist with GenAI expertise.
• Hands-on ML model building, training and fine-tuning large language models.
• Strong background in ML, DL, and modern AI frameworks.

Responsibilities

• Design, develop, and implement advanced LLM-based solutions.
• Own initiatives end-to-end with minimal supervision.
• Stay updated on Generative AI, LLMs, RAG, and applied research.

Skills

GenAI & NLP

LangChain

LlamaIndex

Vector databases

Prompt engineering

Azure OpenAI

Agentic frameworks

RAG systems

NLP fundamentals

Databricks

Tools

LangChain

LlamaIndex

Azure OpenAI

GPT-4

Claude Opus 4.6

Job description

Its fun to work in a company where people truly BELIEVE in what they are doing!

Were committed to bringing passion and customer focus to the business.

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 safe 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, youll enjoy your career with us!

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.