GenAI Lead Engineer (Investment Data Platforms)

We are seeking a highly skilledGenAI Lead Engineerto design and implement advanced frameworks for alternate data analysis in the investment management domain. The candidate will leverageLLM 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 ofMachine Learning Engineers and UI Developersto deliver robust, production-ready solutions. Responsibilities GenAI Framework Development -Develop custom frameworks usingGPT APIs or LLaMAfor alternate data analysis and insights generation. Optimize LLM usage for investment-specific workflows, including data enrichment, summarization, and predictive analysis. Automation & Orchestration -Design and implementdocument ingestion workflowsusing tools such asn8n(or similar orchestration frameworks). Build modular pipelines for structured and unstructured data. Infrastructure & Deployment -Architect deployment strategies oncloud (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 inPythonwith experience in frameworks such asTensorFlow or PyTorch . 2+ years of experience in Generative AI and Large Language Models (LLMs) . Experience withVectorDBs (e.g., Pinecone, Weaviate, Milvus, FAISS)and document ingestion pipelines. Familiarity withdata orchestration tools(e.g., n8n, Airflow, LangChain Agents). Understanding ofcloud deploymentsand GPU infrastructure (CoreWeave or equivalent). Proven leadership skills with experience managingcross-functional engineering teams . Strong problem-solving skills and ability to work in fast-paced, data-driven environments. Experience withfinancial or investment data platforms . Knowledge ofRAG (Retrieval-Augmented Generation)systems. Familiarity withfrontend integrationfor AI-powered applications. Exposure toMLOps practicesfor continuous training and deployment.

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