Gen AI Engineer
About The Position
Tiger Analytics is looking for experienced AI Engineer with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. 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. 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.
Requirements
- Minimum of 7+ years of professional experience in software development and AI engineering
- Hands-on experience integrating Generative AI/LLM APIs, AWS Bedrock, and other model providers
- Experience with DevOps, CI/CD pipelines, and ML pipelines within the AWS ecosystem
- Exposure to building Gen AI/Agentic AI applications, managing efficiency, latency, and backend infrastructure
- Strong Python programming skills
- Deep understanding of OpenAI API standards
- Deep understanding of JSON RESTful design
- Deep understanding of LLM orchestration
- Experience with document chunking and embedding
Nice To Haves
- Experience working with Bedrock Agent/Core services is a significant plus
Responsibilities
- Building high-performance API services
- Implementing complex RAG and Agentic AI architectures
- Designing and implementing end-to-end RAG pipelines, including retrievers, vector stores (e.g., Pinecone, Weaviate, or pgvector), and generators
- Optimizing latency and relevance tuning for production-grade performance
- Developing autonomous or semi-autonomous agents using frameworks such as LangChain, CrewAI, or Semantic Kernel
- Managing orchestration, tool integration, and robust error handling for non-deterministic AI outputs
- Managing memory and context (episodic vs. long-term) in multi-turn interactions and external API interfacing
- Assessing performance, grounding accuracy, and hallucination detection using evaluation frameworks (e.g., RAGAS, TruLens)
- Iterating systems based on performance metrics and continuous improvement practices
Benefits
- Equal employment opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
