Software Engineer

This role involves designing and delivering advanced software solutions with a focus on Agentic AI, Generative AI, and Conversational AI. You will lead the end-to-end product lifecycle, bridging the gap between business requirements and technical execution to drive AI-powered automation. The position requires strong technical leadership to influence decision-making and mentor teams in a hybrid work environment.

We are representing a leading global financial services institution known for its commitment to innovation and excellence in banking. The organization operates at a significant scale, leveraging cutting-edge technology to provide secure and efficient solutions for customers and colleagues worldwide. They foster a culture of collaboration, integrity, and continuous professional growth.

Responsibilities

• Design and implement robust Generative AI and Agentic AI applications using AWS services like Bedrock and SageMaker.
• Architect and deploy LLM-based use cases including RAG, summarization, and advanced data analysis for enterprise-scale applications.
• Fine-tune, evaluate, and optimize Large Language Models to ensure high performance, accuracy, and safety.
• Lead the design and deployment of conversational AI models using Amazon Lex, Amazon Connect, and custom NLP pipelines.
• Collaborate with cross-functional teams including product managers and UX designers to integrate AI capabilities into business workflows.
• Maintain system design integrity and ensure all solutions comply with AI ethics, security standards, and governance policies.
• Provide technical leadership and mentorship to team members while fostering a culture of code quality and knowledge sharing.
• Diagnose and resolve complex production issues while continuously optimizing solutions through research and experimentation.

Requirements

• 9-13 years of professional experience in software engineering with a focus on AI/ML.
• Strong proficiency in Python and AI/ML libraries such as LangChain, LangGraph, Transformers, and CrewAI.
• Deep expertise in developing on AWS, specifically within the AI/ML ecosystem.
• Comprehensive understanding of Agentic architecture, Agentic memory, LLMs, NLP, and Conversational AI.
• Proven experience in prompt engineering, prompt management, and optimizing model outcomes.
• Familiarity with SQL/NoSQL databases, Vector DBs, data chunking, and embedding techniques.
• Experience with REST APIs, EKS, and model deployment/monitoring pipelines.
• Strong communication skills with the ability to influence stakeholders and translate complex technical concepts.