Full Stack Gen AI Engineer

Overview 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 and 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. Job Description 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. Key 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 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: 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 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 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 This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Every individual comes with a different set of skills and qualities, so even if you don\'t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. 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. #J-18808-Ljbffr Tiger Analytics