GenAI Engineer

GenAI Engineer

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, PyTorch 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