Tiger Analytics

Delivers Data and AI transformation

GenAI Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeRemote in CanadaNo salary listedRemote

About the Job

Requirements

At least 5 years of industry experience in software engineering, machine learning, or AI-focused roles.
Hands-on experience with LangGraph and building agentic workflows.
Practical experience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) system design.
Experience fine-tuning large language models, including supervised fine-tuning and reinforcement learning-based methods.
Understanding and application of reinforcement learning concepts in production or research settings.
Background in experimental design, model evaluation, and statistical analysis.
Ability to reduce hallucinations using techniques beyond creative prompting.
Proficiency in Python and experience with modern machine learning and artificial intelligence frameworks.

Responsibilities

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, Model Context Protocol (MCP), and Agent-to-Agent (A2A) communication patterns.
Fine-tune large language models using supervised fine-tuning, reinforcement learning from human feedback, 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 experimentation frameworks, including offline and online evaluations, A/B testing, and metric-driven improvements.
Implement strategies to reduce hallucinations, including retrieval augmentation, grounding, validation layers, confidence scoring, and self-reflection mechanisms.
Collaborate with data engineers, product managers, and platform teams to integrate generative AI solutions into production systems.
Monitor, evaluate, and continuously improve model performance, reliability, latency, and cost.
Stay up to date with research and advancements in generative AI, agentic systems, and model alignment.

GenAI Engineer

Tiger Analytics Inc. · Canada (Remote) · Full-time · Posted 2026-01-19

Workplace: remote

Department: MLE

Description

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.

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About the Company

Tiger Analytics

Delivers Data and AI transformation

Tiger Analytics helps large organizations plan and execute Data and AI–led transformations at scale. It works with clients to translate business needs into data and AI initiatives, using an approach that combines business value mapping, agile operating models, and a platforms-based architecture, backed by partnerships with major hyperscalers. The company designs and delivers end-to-end data and AI solutions—from strategy and data engineering to model development and deployment—so that AI can be applied to tough, real-world problems. Unlike firms that rely mainly on standard playbooks, Tiger Analytics builds custom approaches and accelerators to suit each client’s value chain and operating model. With a global team of 5000+ technologists and consultants, the goal is to enable measurable value and practical outcomes through data and AI programs that work at enterprise scale.

Company size: 1,001-5,000
Company stage: Not listed
Total funding: Not listed
Headquarters: San Jose, CA
Founded: 2010

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