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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.
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, you'll enjoy your career with us!
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