GenAI Engineer job opportunity at Tiger Analytics Inc..

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Date2026-01-19T19:37:32.733Z bot
Tiger Analytics Inc. GenAI Engineer
Experience: 5-years
Pattern: Full-time
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Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI. We are looking for a highly skilled GenAI Engineer with strong hands-on experience in building, evaluating, and deploying advanced Generative AI systems. The ideal candidate will have deep expertise in agentic frameworks, model fine-tuning, and reinforcement learning, along with a strong focus on experimentation, reliability, and hallucination mitigation beyond prompt engineering. Design, build, and deploy end-to-end Generative AI and agentic AI solutions for real-world use cases. Develop and orchestrate multi-agent workflows using LangGraph , MCP (Model Context Protocol) , and A2A (Agent-to-Agent) communication patterns. Fine-tune large language models (LLMs) using supervised fine-tuning (SFT), RLHF, and other advanced techniques to improve task performance and alignment. Apply reinforcement learning approaches to optimize agent behavior, decision-making, and long-horizon tasks. Design and execute rigorous experimentation frameworks , including offline/online evaluations, A/B testing, and metric-driven improvements. Implement robust strategies for hallucination reduction , such as retrieval augmentation, grounding, validation layers, confidence scoring, and self-reflection mechanisms. Collaborate with data engineers, product managers, and platform teams to integrate GenAI solutions into production systems. Monitor, evaluate, and continuously improve model performance, reliability, latency, and cost. Stay up to date with the latest research and advancements in GenAI, agentic systems, and model alignment. Required Qualifications 5+ years of industry experience in software engineering, machine learning, or AI-focused roles. Strong hands-on experience with LangGraph and building agentic workflows. Practical experience with MCP (Model Context Protocol) and A2A (Agent-to-Agent) system design. Proven experience in fine-tuning LLMs , including supervised fine-tuning and reinforcement learning-based methods. Solid understanding and application of reinforcement learning concepts in production or research settings. Strong background in experimental design , model evaluation, and statistical analysis. Demonstrated ability to reduce hallucinations using techniques beyond creative prompting. Proficiency in Python and experience with modern ML/AI frameworks.

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