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