GenAI Engineer

Job Description

Design and develop AI/ML and Generative AI solutions for banking use cases including fraud detection, risk modeling, and customer analytics.

• Build, fine‑tune, and deploy ML models and LLMs for credit scoring, AML, and automation.
• Implement RAG‑based GenAI applications using internal banking data.
• Develop scalable data pipelines for training, validation, and real‑time inference.
• Collaborate with risk, compliance, finance, and business teams for AI solutions.
• Ensure regulatory compliance and AI governance standards.
• Implement data security, privacy, and access control mechanisms.
• Integrate AI models into production using APIs and microservices.
• Apply prompt engineering and model optimization techniques.
• Monitor model performance, drift detection, and continuous improvement.
• Develop explainable AI (XAI) for transparent decision‑making.
• Optimize cost, latency, and scalability of AI systems.
• Troubleshoot AI/ML system issues across data and deployment layers.
• Write efficient Python code using AI frameworks.
• Follow MLOps best practices (CI/CD, automated deployment).
• Ensure responsible AI practices (bias, fairness, ethics).
• Mentor teams and contribute to enterprise AI platforms.

• Languages:
  
  Python
• AI/ML & GenAI:
  Machine Learning, Deep Learning, LLMs, Prompt Engineering, Fine‑tuning
• Frameworks:
  Tensor Flow, Py Torch
• GenAI Tools:
  Lang Chain, Llama Index
• Vector DB:
  Pinecone, FAISS
• Cloud Technologies: AWS, Azure, GCP
• Data Pipelines: ETL/ELT, Real‑time & Batch Processing
• Integration: APIs, Microservices
• Concepts: RAG Architecture, XAI, Model Optimization
• Methodologies:
  Agile/Scrum, MLOps (CI/CD, Model Versioning, Deployment)
• Compliance:
  Banking regulations (SR 11‑7, GDPR), Model Risk Management
• Soft Skills:
  
  Strong communication, stakeholder management, and analytical thinking

$100,000 - $110,000 per year