GenAI Engineer

Job description

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

• TensorFlow, PyTorch

GenAI Tools

• LangChain, LlamaIndex

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 Salary Range- $100,000-$110,000 a year #LI-SP3 #LI-VX1