The job, in the employer's words
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.
Requirements
- 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.
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Benefits mentioned
- Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
- Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Requirements mentioned
- 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.
- 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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