Gen AI Engineer (6-12 years experience) | Factspan Analytics

Gen AI Engineer (6-12 years experience) | Factspan Analytics

Factspan Overview:

Factspan is a pure-play data and analytics services organization. We partner with Fortune 500 enterprises to build an analytics center of excellence, generating insights and solutions from raw data to solve business challenges, make strategic recommendations, and implement new processes that help them succeed. With offices in Seattle, Washington, and Bengaluru, India, we use a global delivery model to service our customers. Our customers include industry leaders from the retail, financial services, hospitality, and technology sectors.

Experience: 6 to 12 years (Overall)

Key Responsibilities

Design and develop LLM-driven applications for use cases such as customer support copilots, merchandising intelligence, document summarization, and internal productivity tools.

Hands-on expertise with Google Cloud Platform (GCP)including deploying, scaling, and managing AI/ML workloads and cloud-native services—is strictly required.

Build scalable and secure RAG pipelines using LangChain, LlamaIndex, or similar frameworks.

Integrate vector databases like FAISS, Weaviate, Milvus, or Pinecone for semantic search.

Work with LLM APIs (OpenAI, GCP, OpenAI, Mistral, Claude) and handle context engineering, chaining, and tool invocation.

Develop APIs and backend services using Python, FastAPI, and manage deployment workflows using Docker/Kubernetes.

Ensure clean logging, rate limiting, and data compliance for enterprise readiness.

Collaborate with DevOps and data engineers to support CI/CD and monitor LLM pipeline performance.

Work in agile delivery mode with daily scrums, sprint planning, and collaborative backlog grooming.

Key Skills:

Strong proficiency in Python and one or more GenAI frameworks (LangChain, LlamaIndex, or Haystack).

Hands-on expertise with Google Cloud Platform (GCP)—including deploying, scaling, and managing AI/ML workloads and cloud-native services—is strictly required.

Solid experience working with LLM APIs and prompt engineering.

Hands-on exposure to vector databases and RAG implementation.

Understanding of token usage, function calling, and context window constraints in LLMs.

Familiarity with MLOps/DevOps deployment in cloud or on-prem environments (preferably GCP)

Ability to work collaboratively with cross-location teams and communicate effectively with onsite stakeholders.

Regards,

Sathish.