- 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.