Staff Engineer GenAI Developer at Visa

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

14 Jul, 26

Salary

0.0

Posted On

15 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, LangChain, Azure, Generative AI, LLM, Vector Databases, RAG, Prompt Engineering, System Design, Agile, Scrum, CI/CD, Jenkins, Jira, Machine Learning, Software Architecture

Industry

IT Services and IT Consulting

Description

Company Description Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters — to you, to your community, and to the world. Progress starts with you. Job Description Overview We are seeking an experienced Staff GenAI Developer to join our Corporate Generative AI CGAI Technologies team which serves as the CIT GenAI Center of Excellence. In this senior role you will drive the development of generative AI solutions covering use cases like intent processing retrieval augmented generation agentic actions and document summarization that empower our employees. You will provide technical leadership across teams to define and enforce best practices, bring a product driven mindset and ensure our GenAI platforms and applications are robust scalable and aligned with business needs. This role requires a hands-on leader with a passion for innovation in AI strong mentorship skills and deep expertise in Python LangChain Azure cloud services and vector databases for AI. Key Responsibilities Technical Architecture & System Design: Work with Architect to Design and implement core components of GenAI systems, including LLM agent orchestration, RAG pipelines, prompt‑engineering frameworks, and model‑fine‑tuning workflows. Make sound architectural decisions, evaluate alternative approaches, and ensure solutions meet performance, reliability, and scalability expectations for enterprise workloads. Hands-on Solution Development: Lead end‑to‑end development of GenAI solutions, from concept through production. Prototype and build critical components in Python, integrate LLMs and frameworks such as LangChain, and operationalize embeddings, vector stores, and RAG pipelines. Ensure AI systems effectively solve real‑world use cases and deliver measurable impact. Cross-Functional Collaboration: Partner with product managers, data scientists, and platform engineers to embed GenAI features into products and workflows. Drive cross‑team alignment, influence architecture across departments, and champion adoption of AI in high‑value use cases. Ensure GenAI efforts remain aligned with the broader enterprise technology strategy. Technical Leadership & Mentorship: Provide senior technical leadership and mentorship to engineers at all levels. Conduct code reviews, support pair programming, guide architectural discussions, and lead micro‑teams or pods to deliver complex features. Foster a collaborative, inclusive, and learning‑oriented engineering culture. Engineering excellence: Establish coding standards, enforce rigorous testing and documentation, and drive consistent CI adoption. Strengthen secure coding practices and maintain high test coverage across components. Ensure all solutions meet enterprise expectations for security, reliability, performance, and compliance. Apply strong engineering fundamentals end‑to‑end: write clean, maintainable, well‑tested code; follow modern design patterns; implement robust unit, integration, and functional tests; and ensure operational reliability through monitoring, observability, and resilient error handling. Review code developed by other engineers and provided actionable feedback to ensure adherence to best practices Agile Project Leadership: Lead and participate in Agile/Scrum ceremonies, including sprint planning, backlog refinement, and retrospectives. Manage work through Jira, optimize deployments via CI/CD pipelines (e.g., Jenkins), remove execution blockers, and maintain alignment across teams to ensure timely delivery of GenAI features. Product Mindset & Innovation: Identify opportunities where GenAI can create user value or improve workflows. Propose, design, and deliver innovative AI‑driven features and improvements. Anticipate future business needs, develop proof‑of‑concepts, and transition them into scalable production solutions. Balance rapid experimentation with sound architecture. Responsible AI & Governance: Ensure all GenAI solutions adhere to responsible AI principles. Integrate privacy, fairness, safety, and security considerations throughout the development lifecycle. Work with governance and security teams to establish guardrails and standard practices for safe enterprise deployment of LLMs. Continuous Learning & Thought Leadership: Stay current with emerging advancements in AI/ML—new LLMs, vector database improvements, open‑source frameworks, and cloud AI capabilities. Evaluate and adopt new techniques that strengthen the platform. Represent the GenAI team in knowledge‑sharing forums and communicate innovations to broader engineering groups and leadership Qualifications Experience: 6+ years of software development experience, including substantial hands-on work in AI/ML or GenAI projects (preferably at least 2–3 years focused on building applications with LLMs or related AI technologies). Prior experience as a tech lead or in a senior engineer role is expected, demonstrating ability to guide teams through complex projects. Technical Expertise: In-depth understanding of large language models and generative AI techniques – including how to implement and optimize LLM-based solutions (prompt engineering, fine-tuning models, working with embeddings and retrieval-augmented generation for knowledge bases). Proven ability to design and build systems that incorporate LLMs, such as AI chatbots, agent workflows, or NLP services. Experience with building AI agents capable of multi-step reasoning or tool use is a strong plus. Programming Skills: Advanced proficiency in Python (for AI/ML development) and familiarity with Java or other object-oriented languages. Experience using frameworks and libraries for GenAI – for example, LangChain, Hugging Face Transformers, or OpenAI APIs – to develop and integrate AI functionalities. Solid understanding of data structures and algorithms; able to write efficient code for both prototyping and production. Cloud & Infrastructure: Hands-on experience with cloud platforms (Azure preferred, or AWS/GCP) and deploying AI solutions at scale. Knowledge of containerization and microservices architecture is beneficial. Ability to work with vector databases or similar technologies for storing embeddings and enabling semantic search is required, as our GenAI applications rely on these for RAG and context. Agile & DevOps: Strong understanding of Agile methodologies and experience working in Scrum teams. Proficiency with tools like Jira for project tracking and CI/CD pipelines (e.g. Jenkins, Git workflows) for automated build and deployment. You should be comfortable with test-driven development and continuous integration practices that ensure rapid yet stable iteration. Leadership & Communication: Excellent leadership skills with a track record of mentoring engineers and influencing teams beyond your own. Capable of setting technical direction and getting buy-in from stakeholders through clear communication and demonstrated expertise. Strong collaboration skills to work cross-functionally; able to translate complex technical concepts into proposals or explanations for product managers and executives. Problem-Solving & Strategic Vision: Demonstrated ability to tackle ambiguous problems and break them down into actionable plans. You anticipate future challenges and opportunities in the GenAI space and craft solutions that not only solve immediate issues but also fit the long-term strategy of the product/team. A knack for balancing quick wins with strategic investments will be important. Education: Bachelor’s degree in computer science, Engineering, or related field. A master’s or PhD in AI/ML or a related specialization is a plus (or equivalent hands-on experience at scale) Additional Information Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law. Job Family Group: Engineering and Technology

Responsibilities

Lead the end-to-end development of generative AI solutions, including RAG pipelines and LLM agent orchestration. Provide technical leadership and mentorship to engineering teams while ensuring solutions are scalable, secure, and aligned with enterprise business needs.