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The role
5-8 years of experience 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.
What you will do
- 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 efficiencyTranslate ambiguous business problems into structured AI solution approaches and measurable outcomesDrive 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-makingCommunicate solution architecture and trade-offs clearly to both technical and non-technical audiencesContribute 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 maintainabilityBuild reusable accelerators, frameworks, and components that can be leveraged across multiple use cases and clientsContribute to internal IP creation (assets, templates, reference architectures)Ensure reliability and robustness of LLM systems through evaluation frameworks, guardrails, and fallback strategiesDesign safe and responsible AI systems (hallucination mitigation, bias handling, governance)Optimize cost-performance trade-offs in large-scale deploymentsIdentify when NOT to use LLMs and propose alternative approachesContribute to code reviews, design reviews, and mentorship of junior team membersDrive quality standards and best practices across projectsStay ahead of advancements in GenAI and proactively evaluate their applicability to business problemsContribute to internal knowledge sharing, training, and capability building.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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About Fractal Analytics
It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.
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