Senior AI Engineer

Vegapay; Bengaluru; On-site; INR 1,600,000 - 2,400,000; Full time

Job summary

Vegapay is seeking a Senior AI Engineer to lead design and development of AI/ML systems for real-time credit decisions, fraud detection, and more.

You will work with a talented team on high-scale banking and payment systems that impact millions. The ideal candidate has strong experience in software engineering, machine learning, and fintech, and will mentor junior engineers while shaping AI product development.

Join Vegapay to help build flexible fintech infrastructure that drives innovation.

Qualifications

• 5+ years in software engineering with at least 3 focused on ML/AI systems.
• Strong foundation in machine learning and model evaluation.
• Proficiency in Python and relevant ML ecosystem tools.

Responsibilities

• Design and build production‑grade ML models for credit underwriting.
• Architect AI inference pipelines integrated with payment flows.
• Lead AI feature roadmap for product domains.

Skills

Software engineering

Machine learning

Python

Data infrastructure

Real-time systems

Fintech experience

AI tools

Tools

PyTorch

TensorFlow

Kafka

Spark

Job description

The Impact You’ll Drive

As a Senior AI Engineer, you will be a senior technical voice on the AI & Data Platform team – independently leading the design, development, and deployment of AI/ML systems that power real-time credit decisions, fraud detection, personalised experiences, operational intelligence and productivity improvements at scale. You will own full problem spaces end‑to‑end, mentor junior engineers, and shape how Vegapay thinks about AI‑native product development.

The Hats You Will Wear

• Design and build production‑grade ML models for credit underwriting, risk scoring, and fraud detection on TB‑scale transaction data.
• Architect and maintain low‑latency AI inference pipelines integrated with UPI payment flows and the CLOU credit lifecycle.
• Lead the AI feature roadmap for one or more product domains – from problem framing through deployment and monitoring.
• Evaluate and integrate LLM and GenAI capabilities into internal tooling, operations automation, and customer‑facing features.
• Build and maintain robust data pipelines (ClickHouse, Spark, Kafka) that feed model training and real‑time inference.
• Define and enforce ML engineering best practices – versioning, experimentation frameworks, model observability, and drift detection.
• Collaborate directly with product, credit, and engineering teams to translate business problems into scalable AI solutions.
• Mentor L1/L2 engineers; conduct design and code reviews; raise the technical bar across the team.

The Perfect Fit

• 5+ years of hands‑on software engineering experience with at least 3 years focused on ML/AI systems in production.
• Strong foundations in machine learning – supervised/unsupervised learning, gradient boosting (XGBoost, LightGBM), deep learning, and model evaluation.
• Proficiency in Python and the ML ecosystem: PyTorch or TensorFlow, scikit‑learn, MLflow or similar experiment tracking.
• Experience with real‑time and batch data infrastructure: Kafka, Spark, Flink, ClickHouse, or equivalent.
• Familiarity with LLM APIs, prompt engineering, RAG architectures, and embedding‑based systems.
• Solid software engineering fundamentals – clean code, distributed systems, REST/gRPC APIs, containerisation (Docker/Kubernetes).
• Experience in fintech, payments, lending, or credit is a strong plus – understanding of risk, compliance, and regulated data environments.
• Customer obsession – you think about end‑user impact, not just model metrics.
• Highest standards – you are not satisfied shipping 80%; you care about correctness, reliability, and edge cases.
• Full ownership – you treat problems as yours until they are solved, not until the code is merged.
• AI‑native – you actively use AI tools in your own workflow and push the team to build AI‑first by default.

Edge Over Rest

• Experience with credit scoring models, bureau data (CIBIL, Experian), or alternative data underwriting.
• Contributions to open‑source ML projects or published research.
• Familiarity with NPCI's UPI ecosystem, RBI regulatory guidelines for digital lending, or CLOU product architecture.

The Problem We’re Solving

Financial institutions today are held back by legacy systems that are slow, rigid, and expensive to scale. Launching or evolving credit, lending, and UPI products often takes months, requires heavy engineering effort, and limits the ability to create personalised customer experiences.

At the same time, customer expectations have changed – speed, flexibility, and tailored financial products are no longer optional. Banks and fintechs need infrastructure that allows them to innovate quickly, adapt continuously, and scale without friction.

This is where we come in.

At Vegapay, we are building modern, configurable fintech infrastructure that enables banks, NBFCs, and enterprises to design, launch, and manage credit and payment programmes with ease. Our platform brings together flexibility, speed, and control – helping our partners unlock new growth opportunities and deliver personalised banking experiences at scale.

The Opportunity Ahead

• Work on real‑world, high‑scale systems in banking, credit, and payments.
• Solve complex engineering problems that directly impact millions of end users.
• Collaborate with strong engineers and product leaders who care about quality and speed.
• High ownership from day one – build, ship, and see your work in production.
• Opportunity to shape systems, not just contribute to them.