Full Stack Gen AI Engineer
Indexed description
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.
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
- 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
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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