Senior AI Engineer

About this role

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Skills you'll use

What you'll do

• Develop an understanding of Payments architecture and its nuances across use cases and countries. This understanding will help build predictive models for payment method recommendations, downtime management, intelligent transaction retry, payments processor selection, and other such solutions. These solutions aim to increase payments transactions and reduce failure rates.
• Leverage advanced LLMs to power the next generation of payment experiences.
• Manage the entire end-to-end lifecycle of predictive models and AI agents, from development to deployment and optimization.
• Work with engineering, compliance & operation teams to create solutions and product changes informed by your findings and business inputs.
• Stay current with the latest AI/LLM research and new frameworks and incorporate them into practical applications.
• Or in a team to solve complex problem statements.
• Individual contributor role with 5+ years of experience expected.
• Predictive Modelling: Build predictive models using machine learning to solve for classification, ranking and online learning. Engineer features from internal data assets to build refined transaction and customer profiles.

Who can apply

• Generative AI Development: Design and deploy Generative AI solutions using prompt engineering, agent orchestration, and model fine-tuning (e.g., LoRA, PEFT). Build evaluation datasets and define guardrails for ongoing evaluation and monitoring.
• MLOps & Automation: Build end-to-end MLOps pipelines to automate model training, deployment, and monitoring. Track production performance and implement feedback loops for continuous improvement.
• Ownership and Collaboration: Design AI solutions by working backwards from our needs. Lead the delivery of AI capabilities, from concept through production deployment.
• Core ML: Expert proficiency in Python, Spark, and SQL (Presto/Hive) with a command of fundamental ML concepts (Bagging, Boosting, Online Learning, Recommendation Engines).
• Generative AI & Agent Frameworks: Hands-on experience with Agentic frameworks (specifically LangGraph, LangSmith, or LangChain) and LLM orchestration.
• MLOps & Productionization: Expertise in productionizing ML solutions with MLOps tools (MLflow, Kubeflow, TFX, SageMaker) and deploying scalable components on cloud platforms.
• Real-Time Streaming: Experience in the real-time implementation of machine learning solutions using Flink SQL and Flink SDK is a good to have
• We have your back with Term Life Insurance and comprehensive Medical Insurance.

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