Gen AI Engineer

Tiger Analytics Inc.; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Tiger Analytics is seeking an experienced AI Engineer to join our global team. The role focuses on building high-performance API services with a heavy emphasis on Python, AWS Bedrock, and Generative AI to deliver scalable AI solutions.

You will design and implement end-to-end RAG pipelines, develop autonomous agents, and optimize systems for latency and reliability. Expertise in OpenAI API standards and LLm orchestration is required, with Bedrock Agent/Core exposure a strong plus.

Qualifications

• Strong Python programming skills and experience in AI engineering.
• Hands-on experience integrating Generative AI/LLM APIs and AWS Bedrock.
• Experience with DevOps, CI/CD pipelines, and ML pipelines in AWS.
• Exposure to Gen AI/Agentic AI applications and backend infrastructure.
• Familiarity with OpenAI API standards, JSON RESTful design, and LLM orchestration.

Responsibilities

• Design and implement high-performance API services.
• Build end-to-end Retrieval-Augmented Generation (RAG) pipelines.
• Develop autonomous or semi-autonomous agents and manage orchestration.
• Optimize latency and performance; ensure robust error handling.
• Evaluate AI systems using established frameworks and iterate improvements.

Skills

Python

Generative AI

OpenAI API

LLM orchestration

DevOps

AWS

Tools

LangChain

CrewAI

Semantic Kernel

Pinecone

Weaviate

pgvector

Bedrock

Job description

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.

Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore, and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. We are a Great Place to Work-Certified™ company, recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG, and others. We are looking for a highly skilled AI Engineer with 7+ years of experience in software engineering, with a heavy focus on Python, AWS infrastructure, and Generative AI. The ideal candidate will be responsible for building high-performance API services and implementing complex RAG and Agentic AI architectures.

Key Requirements

• Experience: Minimum of 7+ years of professional experience in software development and AI engineering.
• Generative AI Integration: Hands-on experience integrating Generative AI/LLM APIs, AWS Bedrock, and other model providers.
• Infrastructure & DevOps: Experience with DevOps, CI/CD pipelines, and ML pipelines within the AWS ecosystem.
• Agentic AI: Exposure to building Gen AI/Agentic AI applications, managing efficiency, latency, and backend infrastructure.
• Technical Standards: Strong Python programming skills with a deep understanding of OpenAI API standards, JSON RESTful design, and LLM orchestration.
• Preferred Skills: Experience working with Bedrock Agent/Core services is a significant plus.

Core Focus Areas & Expectations

Candidates will be expected to demonstrate deep technical proficiency in the following areas:

1. Retrieval-Augmented Generation (RAG)

• Ability to design and implement end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators.
• Expertise in latency optimization and relevance tuning to ensure production-grade performance.
• Strategic approach to document chunking and embedding, balancing granularity with semantic coherence.

2. Agent Development

• Practical experience developing autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel.
• Ability to manage orchestration, tool integration, and robust error handling for non-deterministic AI outputs.
• Proficiency in managing memory and context (episodic vs. long-term) in multi-turn interactions and external API interfacing.

3. Evaluation and Optimization

• Familiarity with evaluation frameworks (e.g., RAGAS, TruLens) to assess performance, grounding accuracy, and hallucination detection.
• Ability to iterate systems based on performance metrics and continuous improvement practices.

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.