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.