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 top-notch talent as we continue to build the best global analytics consulting team in the world.
Role Overview
The AI Engineer drives the on-site deployment, integration, and scaling of our enterprise Generative AI solutions. This role embeds directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across Azure as cloud. You will bridge the gap between AI research and production-grade cloud infrastructure.
You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.
Requirements
- Requirements Expertise: Proven track record of producing clear, precise, and structured requirement documents (PDD, SDD, User Stories).
- UiPath Familiarity: Working understanding of the UiPath ecosystem (Studio, Orchestrator, Document Understanding, Action Center).
- Azure AI & AI Concepts: Familiarity with Azure AI concepts
- Automation Engineering: Strong grasp of software automation principles, API integrations, software development lifecycle (SDLC) methodologies (Agile/Scrum), and system architecture basics.
- Process Mapping: Proficiency in process mapping tools (e.g., Microsoft Visio, Lucidchart, Signavio, or Bizagi).
Required Qualifications
- 7+ years of industry experience in software engineering, machine learning, or AI-focused roles.
- Strong hands-on experience with LangGraph and building agentic workflows.
- Proven experience in fine-tuning LLMs, including supervised fine-tuning and reinforcement learning-based methods.
- 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
- Requirements Expertise: Proven track record of producing clear, precise, and structured requirement documents (PDD, SDD, User Stories).
- UiPath Familiarity: Working understanding of the UiPath ecosystem (Studio, Orchestrator, Document Understanding, Action Center).
- Azure AI & AI Concepts: Familiarity with Azure AI concepts
- Automation Engineering: Strong grasp of software automation principles, API integrations, software development lifecycle (SDLC) methodologies (Agile/Scrum), and system architecture basics.
- Process Mapping: Proficiency in process mapping tools (e.g., Microsoft Visio, Lucidchart, Signavio, or Bizagi).
- 7+ years of industry experience in software engineering, machine learning, or AI-focused roles.
- Strong hands-on experience with LangGraph and building agentic workflows.
- Proven experience in fine-tuning LLMs, including supervised fine-tuning and reinforcement learning-based methods.
- 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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