Full Stack Gen AI Engineer

Tiger Analytics Inc.•Dallas, TX

About The Position

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 Full Stack Gen 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.

Requirements

Experience: Minimum of 7+ years of professional experience in software development and AI engineering.
API & Backend: Expert in building high-performance API / microservices using Python (FastAPI) deployed on AWS Fargate (ECS) (Most Critical).
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.
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.
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.
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.