GenAI Lead Engineer

4 days ago

Delhi, 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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