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.
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.
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.