Fetcherr specializes in deep learning, algorithmic forecasting, and dynamic markets. The company is building a new generation of LLM-powered analytical systems that will transform how businesses interact with data.

As the Tech Lead for LLM Systems, you will architect and build Fetcherr’s LLM-based virtual analyst platform from the ground up. This is a highly technical, hands-on role focused on LLM architecture, RAG pipelines, prompt engineering, vector databases, and reliability infrastructure.

You’ll work with product, data, and engineering teams to design a powerful conversational analytics engine built on state-of-the-art LLM technologies.

Responsibilitie

sArchitecture & System Desig

• nArchitect end-to-end systems that combine
• :LLMs (commercial + open-source
• )Vector databases & embedding
• sRetrieval-augmented generation (RAG
• )Natural-language-to-SQL/query generatio
• nVisualization and analytics librarie
• sDefine the technical roadmap and system architecture for the LLM platform

.Hands-On Technical Executio

• nBuild prototypes, run experiments, evaluate LLM providers (OpenAI, Claude, Gemini)
• .Develop and optimize
• :Prompt engineering & chainin
• gRAG pipeline
• sQuery translation engine
• sAuto-generated dashboards and visual output
• sEnsure scalability, observability, security, and cost efficiency

.LLM Expertise & Innovatio

• nLead research and experimentation for model selection, fine-tuning, context injection, and multi-agent tooling
• .Evaluate open-source vs. commercial LLMs and architect hybrid solutions
• .Define evaluation frameworks for accuracy, hallucinations, latency, and safety

.Organizational LLM productivity evangelist

• Lead the organizational adoption of AI tools for improving productivity and qualit

yCross-Functional Technical Leadershi

• pWork closely with product managers and customers to shape use cases
• .Provide technical mentorship to LLM engineers, backend developers, and data engineers
• .Be the internal expert and advocate for LLM technologies across Fetcherr

.
Requirement

• s:Deep knowledge of LLM system architectur
• e:RAG, embeddings, vector sear
• chPrompt engineering & prompt chaini
• ngData-to-text generation & visualization too
• lsPython, backend systems, cloud infrastructure (GCP preferre
• d)Experience with LLM frameworks (LangChain, LlamaIndex, Hugging Face
• ).Experience with APIs like OpenAI, Anthropic Claude, Gemini, et

c.Leadership & Product Thinki

• ngExperience delivering complex 0→1 AI system
• s.Ability to translate business needs into technical architecture
• s.Strong communication skills for explaining LLM decisions and trade-off

s.Nice to Ha

• veExperience with BI/analytics or natural language interfaces to dat
• a.Knowledge of multi-agent systems or tool-augmented LLM
• s.Background in airline, pricing, or financial domain