Director (Technology Leader Application Development,GenAI,Python,Java)

Visa Consolidated Support Services India; Bengaluru; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

Visa Consolidated Support Services India in Bengaluru is seeking a senior AI engineering leader to drive GenAI initiatives, architecture, and delivery across enterprise platforms. You will guide teams in designing scalable AI solutions, integrating LLMs, RAG, and model customization to meet business needs.

The role emphasizes cloud-native microservices, secure practices, and collaboration with product, security, and platform teams to align AI deployments with enterprise strategy.

Qualifications

• 12–14 years of software engineering experience with AI/ML leadership.
• Expertise in LLMs, embeddings, RAG, and GenAI frameworks.
• Proficiency in Python and enterprise languages (Java/Scala/Go).
• Experience building cloud-native microservices on AWS/Azure/GCP.
• Hands-on with Kubernetes, Docker, CI/CD, and observability.
• Strong leadership and cross-functional collaboration.

Responsibilities

• Lead GenAI design, development, and deployment including LLM integration.
• Prototype innovative AI solutions to accelerate business transformation.
• Architect cloud-native, high-availability applications.
• Oversee performance, caching, and monitoring.
• Ensure secure, compliant engineering practices.
• Design microservices with domain-driven and event-driven patterns.
• Implement robust interservice communication and reliability patterns.
• Mentor teams in AI/GenAI adoption and architectural excellence.
• Translate business goals into AI roadmaps.
• Define coding standards and model governance.
• Collaborate with product, security, and platform teams.

Skills

GenAI expertise

LLMs & embeddings

Python

Java/Scala/Go

Cloud-native systems

Kubernetes & Docker

MLOps

Cross-functional leadership

Tools

Kubernetes

Docker

CI/CD tools

Observability tools

Job description

Key Responsibilities

• Lead the design, development, and deployment of GenAI solutions, including LLM integration, prompt engineering, RAG systems, and domain-specific model customization.
• Evaluate emerging AI capabilities and prototype innovative solutions to accelerate business transformation. Drive experimentation frameworks that validate model performance, user experience impact, and production feasibility.
• Architect cloud-native applications capable of handling large-scale, high-availability workloads.
• Oversee performance optimization, distributed processing, caching strategies, and operational monitoring.
• Ensure secure, compliant, and resilient engineering practices aligned with enterprise standards.
• Design and evolve microservices architectures using best practices such as domain-driven design, event-driven patterns, and API-first development.
• Implement robust interservice communication (REST, gRPC, messaging) and reliability patterns (circuit breakers, rate limiting).
• Guide engineers on scaling, testing, and maintaining distributed services.
• Mentor engineering and data science teams in modern AI, GenAI adoption, and architectural excellence.
• Translate business goals into actionable engineering roadmaps and AI solution blueprints.
• Define coding standards, architecture guidelines, model governance, and delivery best practices.
• Collaborate closely with product, security, and platform teams to align AI solutions with enterprise strategy.

Qualifications

• 12-14 years of software engineering experience, including significant leadership in AI/ML and GenAI initiatives.
• Expertise in LLMs, embeddings, RAG systems, model finetuning, and GenAI frameworks (LangChain, LlamaIndex, Hugging Face, etc.).
• Strong proficiency in Python plus enterprise languages (Java/Scala/Go).
• Experience building scalable cloud-native systems (AWS, Azure, GCP) and microservices architectures.
• Hands-on experience with Kubernetes, Docker, CI/CD pipelines, and observability tools.
• Deep understanding of MLOps practices, model lifecycle management, and production AI monitoring.
• Proven ability to deliver large-scale analytical applications and distributed data workflows.
• Strong leadership, communication, and cross-functional collaboration skills.