GenAI Lead Engineer (Investment Data Platforms)

We are seeking a highly skilled GenAI Lead Engineer to design and implement advanced frameworks for alternate data analysis in the investment management domain.

The candidate will leverage LLM APIs (GPT, LLaMA, etc.) , build scalable orchestration pipelines, and architect cloud/private deployments to power next-generation AI-driven investment insights.

This role will also involve leading a cross-functional team of Machine Learning Engineers and UI Developers to deliver robust, production-ready solutions.
Responsibilities
GenAI Framework Development - Develop custom frameworks using GPT APIs or LLaMA for alternate data analysis and insights generation.

Optimize LLM usage for investment-specific workflows, including data enrichment, summarization, and predictive analysis.
Automation & Orchestration - Design and implement document ingestion workflows using tools such as n8n (or similar orchestration frameworks).

Build modular pipelines for structured and unstructured data.
Infrastructure & Deployment - Architect deployment strategies on cloud (AWS, GCP, Azure) or private compute environments (CoreWeave, on-premises GPU clusters) .

Ensure high availability, scalability, and security in deployed AI systems.
Required Candidate Profile
Strong proficiency in Python with experience in frameworks such as TensorFlow or PyTorch .
2+ years of experience in Generative AI and Large Language Models (LLMs) .
Experience with VectorDBs (e.g., Pinecone, Weaviate, Milvus, FAISS) and document ingestion pipelines.
Familiarity with data orchestration tools (e.g., n8n, Airflow, LangChain Agents).
Understanding of cloud deployments and GPU infrastructure (CoreWeave or equivalent).
Proven leadership skills with experience managing cross-functional engineering teams .
Strong problem-solving skills and ability to work in fast-paced, data-driven environments.
Experience with financial or investment data platforms .
Knowledge of RAG (Retrieval-Augmented Generation) systems.
Familiarity with frontend integration for AI-powered applications.
Exposure to MLOps practices for continuous training and deployment.