Senior Software Engineer – AI Solutions

Job Purpose

We are looking for an experienced Senior Software Engineer to design, develop, and deliver enterprise-grade AI solutions for different Banking Domains.

You will work on a diverse range of innovative solutions including conversational AI, Retrieval-Augmented Generation (RAG) platforms, AI agents/Agentic AI, intelligent document processing, workflow automation, knowledge management systems, and enterprise integrations.

Working closely with Solution Architects, and Technical Leads. You will build scalable, secure, and production-ready applications that solve real business challenges while leveraging modern cloud and AI technologies.

Key Responsibilities

Enterprise AI solution delivery

• Translate approved business use cases and solution designs into secure, scalable and maintainable enterprise applications.
• Develop backend services, microservices, RESTful APIs and, where required, responsive web interfaces using the approved technology stack.
• Build and enhance conversational AI, RAG, semantic search, AI-agent, intelligent document processing, knowledge management and workflow automation capabilities.

Integration, data and security

• Integrate applications with enterprise data services, enterprise databases, vector
• stores, model platforms, event or messaging services, identity platforms and third party APIs.
• Implement enterprise controls for authentication and authorisation, secrets encryption, sensitive-data handling, audit logging, AI guardrails and secure software development.

Engineering quality and operations

• Create automated tests and AI evaluation checks; monitor quality, performance, latency, availability, safety and cost-related engineering indicators.
• Package and deploy applications through approved CI/CD pipelines, containers and Kubernetes-based environments across development, testing and production.
• Troubleshoot defects and production incidents, complete root-cause analysis and implement durable corrective actions within agreed service and delivery timelines.

Collaboration and continuous improvement

• Participate in architecture and technical-design reviews, document solutions, review code and contribute to reusable engineering standards and components.
• Work closely with Product Owners, Solution Architects, Data Scientists, Cybersecurity, Data Governance, DevOps, Infrastructure, UI/UX, business teams and delivery partners;
• Evaluate emerging technologies where they provide measurable business value.

Qualifications

Bachelor’s Degree in Computer Science / Information Technology or equivalent.

Professional Certifications

Relevant certification in Generative AI/Agentic AI , cloud platforms (Alibaba Cloud, Azure or AWS), Kubernetes, DevSecOps, secure software development, data engineering will be an added advantage.

Relevant Work Experience

• Minimum 5 years of professional software engineering experience, including delivery and support of production applications.
• At least 2 years of hands-on experience with Generative AI, RAG, machine learning or intelligent automation, delivery of at least multiple production-grade AI application is highly desirable.
• Strong hands-on proficiency in Python. Experience with Java and Node.js for enterprise integration is desirable.
• Experience with modern web development using React, Vue.js or Angular.
• Strong understanding of application architecture, object-oriented programming, design patterns, clean code, RESTful APIs and microservices.
• Hands-on experience with SQL, NoSQL and vector databases, Git-based development, automated testing, Docker, Kubernetes or containerized development, CI/CD and Agile delivery.
• Practical experience with RAG pipelines, AI agents, LLM orchestration frameworks, LLM evaluation, observability, security controls and production troubleshooting is highly desirable.

Competencies/Skills

• Core engineering: advanced Python, backend development, API and microservice design, clean code, automated testing and code review; Java or Node.js is beneficial.
• RAG and data: ingestion, OCR and parsing, chunking, embeddings, metadata, vector indexing, hybrid retrieval, reranking, citations and document-level access control.
• Agentic AI and LLMOps tool or MCP integration, orchestration, prompt and configuration versioning, evaluation, deployment, rollback, tracing and human-in-the-loop controls.
• Platform and reliability: cloud services, Docker, Kubernetes, CI/CD, observability, token and cost monitoring, performance optimisation, resilience and production support.
• Security and integration: OAuth2, OpenID Connect, SAML, Microsoft Entra ID, secure SDLC, sensitive-data protection, AI threat controls, event-driven architecture and enterprise API integration.
• Behavioral: ownership, analytical problem solving, attention to detail, clear communication, stakeholder collaboration, continuous learning and focus on measurable business outcomes.