Search & AI Retrieval Lead

 

Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employees, customers, and other stakeholders. 

Consulting and private equity firms come to proSapient when they need to discover knowledge to help them make great decisions and succeed in their goals. It is our mission to support them in their discovery of knowledge. 

We help our clients find industry experts who can provide their knowledge via interview or survey: we curate this knowledge in a market-leading software platform; and we help clients surface knowledge they already have through expansive knowledge management.  

 

As our Search & AI Retrieval Lead, you’ll define how structured data, semantic understanding, and intelligent systems come together to power discovery across our platform. From knowledge graphs to LLM-driven enrichment and recommendation systems, you’ll be at the forefront of building something genuinely cutting-edge — with real impact on how our users experience and navigate information. 

 

The key duties of this role will include: 

Search Architecture Strategy: 

• Evaluate and define the future search stack (technology selection is part of the role). 

• Design a scalable, cloud-native search architecture from first principles. 

• Decide between and combine approaches such as: 

• Keyword search 

• Vector search 

• Hybrid retrieval 

• Graph-based retrieval 

• LLM re-ranking 

• Design indexing, data modeling, and ranking strategies. 

• Define measurable relevance and quality frameworks. 

Knowledge Graph & Intelligent Retrieval: 

• Design and implement a Knowledge Graph architecture modelling entities and relationships. 

• Define graph schema, linking strategies, and enrichment workflows. 

• Combine graph-based signals with semantic search and ranking models. 

• Build extraction pipelines on top of frontier models (OpenAI, Gemini). 

• Develop modern recommendation systems leveraging structured + unstructured data. 

Data & Cloud Infrastructure (GCP-first): 

• Architect retrieval pipelines on Google Cloud Platform (GCP). 

• Design data pipelines and feature stores using BigQuery or alternative analytical systems. 

• Ensure scalability, observability, and cost efficiency. 

• Build production-grade systems, not experiments. 

 

 Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employee...

• 5+ years building and scaling production search or retrieval systems. 

• Strong experience designing search architectures, not just operating them. 

• Experience evaluating and selecting search technologies. 

• Experience with semantic search and embeddings-based retrieval. 

• Experience building pipelines on top of frontier LLMs (OpenAI, Gemini). 

• Strong Python skills. 

• Experience leading technical initiatives in mid-sized teams. 

• Experience working with cloud-native systems (preferably GCP). 

 

Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employees, customers, and other stakeholders. 

Consulting and private equity firms come to proSapient when they need to discover knowledge to help them make great decisions and succeed in their goals. It is our mission to support them in their discovery of knowledge. 

We help our clients find industry experts who can provide their knowledge via interview or survey: we curate this knowledge in a market-leading software platform; and we help clients surface knowledge they already have through expansive knowledge management.  

 

As our Search & AI Retrieval Lead, you’ll define how structured data, semantic understanding, and intelligent systems come together to power discovery across our platform. From knowledge graphs to LLM-driven enrichment and recommendation systems, you’ll be at the forefront of building something genuinely cutting-edge — with real impact on how our users experience and navigate information. 

 

The key duties of this role will include: 

Search Architecture Strategy: 

• Evaluate and define the future search stack (technology selection is part of the role). 

• Design a scalable, cloud-native search architecture from first principles. 

• Decide between and combine approaches such as: 

• Keyword search 

• Vector search 

• Hybrid retrieval 

• Graph-based retrieval 

• LLM re-ranking 

• Design indexing, data modeling, and ranking strategies. 

• Define measurable relevance and quality frameworks. 

Knowledge Graph & Intelligent Retrieval: 

• Design and implement a Knowledge Graph architecture modelling entities and relationships. 

• Define graph schema, linking strategies, and enrichment workflows. 

• Combine graph-based signals with semantic search and ranking models. 

• Build extraction pipelines on top of frontier models (OpenAI, Gemini). 

• Develop modern recommendation systems leveraging structured + unstructured data. 

Data & Cloud Infrastructure (GCP-first): 

• Architect retrieval pipelines on Google Cloud Platform (GCP). 

• Design data pipelines and feature stores using BigQuery or alternative analytical systems. 

• Ensure scalability, observability, and cost efficiency. 

• Build production-grade systems, not experiments. 

 

 Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employee...

• 5+ years building and scaling production search or retrieval systems. 

• Strong experience designing search architectures, not just operating them. 

• Experience evaluating and selecting search technologies. 

• Experience with semantic search and embeddings-based retrieval. 

• Experience building pipelines on top of frontier LLMs (OpenAI, Gemini). 

• Strong Python skills. 

• Experience leading technical initiatives in mid-sized teams. 

• Experience working with cloud-native systems (preferably GCP).