Senior Platform Engineer - Core Platforms & AI

Key Responsibilities:

• Design, build, and own reusable Core Platform capabilities that provide common building blocks for engineering teams across products and functions.
• Take ownership of platform initiatives end-to-end - from understanding the problem and designing the solution to implementation, deployment, monitoring, and ongoing production support.
• Design and implement scalable, reliable services and APIs using modern software engineering and distributed-systems principles.
• Modernize existing platform components, addressing technical debt and legacy patterns while improving scalability, reliability, maintainability, and developer experience.
• Build and integrate AI-powered platform capabilities, including LLM applications, RAG pipelines, agentic workflows, model integrations, and AI-enabled services.
• Prototype new technologies and ideas quickly, validate them through working implementations, and take successful solutions toward production.
• Make pragmatic technical decisions around architecture, technology choices, design patterns, and build-vs-buy, while keeping long-term maintainability in mind.
• Improve platform performance, reliability, scalability, security, and observability across production systems.
• Establish engineering practices and reusable patterns that enable teams to build consistently on the platform.
• Work closely with engineering, data science, product, and other stakeholders to understand requirements and translate them into robust technical solutions.
• Troubleshoot complex production issues and drive them through to resolution.
• Act as a technical owner and hands-on engineering leader for critical platform initiatives, without being limited to a specific product or functional area.

Must-Have Technical Skills:

• Strong hands-on experience with Python or Java, with a track record of designing and building production-grade services, REST APIs, and microservices.
• Strong understanding of system design, distributed systems, scalability, reliability, and software engineering fundamentals.
• Experience with AWS or another major public cloud, along with Docker and Kubernetes.
• Strong experience with SQL and NoSQL databases and understanding of data access and persistence patterns.
• Experience with Git, CI/CD, automated testing, and production troubleshooting.
• Experience with messaging and event-streaming systems such as Kafka, Pulsar, or similar.
• Hands-on experience building and deploying at least one LLM-powered application, such as a RAG or agentic application, using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
• Ability to independently take a technical problem from problem definition -> design -> implementation -> deployment -> production ownership.

Good To Have Skills:

• Broader exposure to the agentic AI ecosystem, including multiple frameworks, MCP or similar tool-integration patterns, prompt engineering, and model orchestration.
• Exposure to data engineering technologies such as Spark, Airflow, or similar.
• Experience with observability and platform operations, including Prometheus, Grafana, OpenTelemetry, ELK, or similar.
• Understanding of IAM, security, and secure platform design.
• Exposure to MLOps, model evaluation, AI guardrails, and AI observability.
• Experience deploying or managing open-source LLMs.
• Experience with fintech or other regulated, large-scale distributed systems.
• Open-source contributions or active participation in the technical community.

What We're Looking For:

• Builder first: You enjoy writing production code and solving real engineering problems, not just producing design documents.
• End-to-end ownership: You are comfortable owning a problem from initial understanding through design, implementation, deployment, and production operation.
• Strong system thinker: You can reason about the larger platform while still diving deep into code and implementation details.
• Pragmatic engineer: You make technology choices based on the problem, rather than chasing technology for its own sake.
• Independent owner: Once the objective is clear, you can define the approach, drive execution, and make the necessary technical decisions.
• Fast learner: You are comfortable evaluating emerging technologies and turning promising ideas into working prototypes and production capabilities.
• Collaborative technical leader: You can influence engineers and stakeholders through strong technical thinking, hands-on contribution, and clear communication.
• Product and platform mindset: You build reusable capabilities that solve problems across multiple teams rather than one-off solutions for a single use case.

Why Join Us?:

• Own and shape Core Platforms that are fundamental to our fintech products.
• Take ideas from concept and design all the way to production with significant technical ownership.
• Work on high-impact platform initiatives spanning cloud-native engineering, distributed systems, data, and AI.
• Build real-world AI capabilities including LLM applications, RAG, agentic systems, and AI platform services.
• Work across multiple engineering and product areas, with the opportunity to influence how technology is built across the organization.
• Join a team where hands-on engineering, ownership, and pragmatic technical decision-making are valued.

Skills

AWS, AutoGen, CrewAI, Docker, Java, Kubernetes, LLM, LangChain, LangGraph, Python