Software Engineer (1 - 2 years of experience in Python, GenAI, ML, Deep Learning) at Visa in IN - Bengaluru, India

Job description

• Visa AI
• Studio is Visa's AI operating system: a single platform for building, deploying, and operating predictive models, foundation models, and AI agents at global scale.
• It gives every team at Visa a common, self service way to train and experiment, build with generative AI, develop and run production AI agents, manage features and memory, and operate everything with governance and observability built in. Visa AI
• Studio is the foundation for how AI gets built across the company. We are looking for an AI
• Engineer to join the AI
• Engineering Platform team within Visa AI
• Studio, working on the systems that let every team at Visa train, deploy, and operate models and agents themselves. This is a modern AI engineering role: it sits deliberately at the intersection of core AI/ML knowledge and systems and software engineering.
• You need to understand how models actually work, including architectures, training dynamics, embeddings, retrieval, evaluation, and the behavior and failure modes of agents that plan and call tools.
• And you need to be equally fluent in the systems side: distributed computing, large scale training and batch systems, API and SDK design, observability, and infrastructure that holds up at scale. Neither half is optional; the platform work only makes sense when both are in the same head.
• You should also be AI native in how you build: comfortable pairing with coding agents, LLM powered tooling, and automated evaluation to design and ship the platform itself faster.
• The role is intentionally forward looking. You won't just be operating today's stack, you'll be shaping how models, agents, and foundation models get built, served, and governed as the underlying techniques keep evolving. We want engineers who track the frontier of AI research and translate it into platform capability faster than the rest of the industry.

Responsibilities

• Design, build, and operate core components of the AI Engineering Platform: the Batch Platform.
• Design, build, and operate the Batch Platform: large scale scheduled and on demand batch scoring, offline model execution, and high throughput data pipelines for training and feature generation.
• Build self-service APIs, SDKs, and tooling that let engineers and data scientists across Visa train, fine tune, deploy, evaluate, and monitor models and agents without platform team intervention.
• Design and extend agent runtimes and orchestration primitives, including tool calling, memory, planning, and multi-agent coordination, for agents that operate safely and predictably in production. Apply core ML and deep learning knowledge (model architectures, embeddings, fine tuning, evaluation methodology) to platform design decisions, not just infrastructure decisions.
• Build and maintain infrastructure that supports large scale distributed training, high throughput batch processing, and efficient GPU and compute cluster utilization.
• Design cost and efficiency optimizations for training and batch workloads, such as distributed scheduling, caching, quantization, and compute right sizing.
• Embed governance, responsible AI, audit, and monitoring capabilities directly into platform components, including drift, hallucination, and anomaly detection for agentic systems.
• Build and evolve the cloud native infrastructure underpinning the platform's compute, storage, and orchestration layers.
• Partner with the AI Context Platform, AI Runtime Services, and AI Governance Platform teams to deliver a coherent, end to end platform experience.
• Stay current with AI research and the broader platform/tooling ecosystem and bring in techniques and patterns before they become standard practice.
• Use AI coding agents and LLM powered developer tools as part of your own workflow to design, build, and ship AI Studio capabilities faster, treating AI assisted engineering as a core skill, not a side habit.
• Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.

Requirements

• Bachelor's degree, OR 3+ years of relevant work experience
• Bachelor's degree, OR 3+ years of relevant work experience
• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• Master's degree or PhD in a relevant technical field.
• Professional experience in software engineering, ML engineering, or platform/infrastructure engineering.
• Solid understanding of core machine learning and deep learning concepts: model training, evaluation, transformer/foundation model architectures, and embeddings. Strong systems and software engineering fundamentals: distributed systems, API/SDK design, and cloud infrastructure (e.g., Kubernetes and a major cloud provider such as AWS, Azure, or GCP).
• Proficiency in at least one language commonly used in AI/platform engineering (e.g., Python, Java).
• Ability to reason about a model or agent across its full lifecycle, from training through production deployment, monitoring, and retirement.
• Hands on fluency with modern AI assisted development, using coding agents, copilots, and LLM based tooling to accelerate day to day engineering work.
• Experience building with or on top of foundation models / large language models: prompting, fine tuning, retrieval augmented generation (RAG), and evaluation frameworks.
• Experience designing or operating AI agent systems: tool calling, multi agent orchestration, memory systems, or agent frameworks.
• Experience building internal developer platforms, SDKs, or self-service tooling used by other engineering or data science teams.
• Experience with feature stores, embedding/vector systems, or knowledge graph and memory systems used by ML or agentic applications.
• Experience with training and batch efficiency techniques such as distributed scheduling, quantization, caching, or compute right sizing at scale.
• Experience operating production systems at high scale, such as large-scale distributed training jobs or high volume batch processing pipelines.
• Experience implementing AI governance, responsible AI, or model risk/compliance controls within a platform. Active engagement with current AI research and open source tooling (e.g., new model architectures, agent frameworks, evaluation methods).

Skills

Required

• Python
• Java
• Kubernetes
• AWS
• Azure
• GCP
• Machine Learning
• Deep Learning
• Generative AI
• Foundation Models
• LLMs
• RAG