GenAI Lead Engineer

2 days ago

north delhi, India Vichara Technologies Full time

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.

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