Machine Learning Engineer for Mortgage Loan Automation

1 day ago

Anand, Gujarat, India beBeeArtificial Full time US$ 1,20,000 - US$ 1,70,000
Lead AI Developer Position

We are seeking a highly skilled Lead AI Developer to join our team focused on automating US mortgage loan processing workflows.

  • The ideal candidate will design and implement machine learning (ML), natural language processing (NLP), large language model (LLM), and agentic AI pipelines for automating loan origination, underwriting, and document intelligence processes.
Key Responsibilities:
  • Develop and deploy LLM-based solutions using tools like Ollama, OpenAI, HuggingFace, and integrate via LangChain or similar frameworks.
  • Build and orchestrate agentic AI systems (e.g., using LangChain Agents, AutoGen, CrewAI) to enable autonomous decision-making, task planning, and loan-processing agents.
  • Fine-tune domain-specific LLMs and embeddings for intelligent document classification, summarization, and question answering.
  • Develop end-to-end MLOps workflows for scalable training, testing, and monitoring of models.
  • Deploy AI models and microservices using Azure (Azure ML, Functions, Blob Storage, App Services).
  • Work with cross-functional teams to ensure compliance, explainability, and effectiveness of AI solutions.
  • Leverage prompt engineering, RAG (Retrieval-Augmented Generation), and vector stores for contextual mortgage document workflows.
Required Skills & Qualifications:
  • 5+ years of hands-on experience in machine learning, NLP, or LLM-based systems.
  • Proven expertise with LLMs (e.g., OpenAI, Ollama, GPT-4, Mistral) and LangChain / AutoGen / Agentic AI design.
  • Proficiency in Python, Scikit-learn, PyTorch/TensorFlow, Spacy, HuggingFace Transformers.
  • Strong knowledge of Azure Cloud – including Azure ML, Azure Functions, Azure DevOps.
  • Experience with MLOps tools like MLflow, Azure ML pipelines, or Kubeflow.
  • Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate).
  • Experience deploying models into production-scale environments.

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