• Design, develop, and maintain scalable backend services using Java and Spring, with increasing contributions to Python-based systems. • Provide technical leadership on architecture and system design in a multi-service, cloud-based environment. • Apply strong software engineering fundamentals to produce clean, maintainable, and well-tested code. • Partner with product, data, and engineering stakeholders to define and deliver complex features and platform capabilities. • Lead by example through code reviews, design discussions, and technical decision-making. • Identify and address performance, reliability, and scalability issues across services. • Mentor engineers and help elevate engineering best practices across the organization. • Stay current with relevant technologies and thoughtfully introduce improvements where they provide clear value.

• Designing and developing LLM training platform. • Maintaining our ML infrastructure, ensuring optimal performance, scalability and reliability. • Improving job scheduling strategies to minimize resource fragmentation.

• Building declarative trading engines so users can compose strategies directly from the UI • Designing automated portfolio construction and rebalancing logic • Integrating with yield vaults, liquidity sources, AMMs, and RWAs • Creating calm, intuitive UX on top of complex onchain primitives • Building scalable infrastructure for long-term asset management

• Design model training and inference workflows with clear versioning, lineage, and promotion criteria where models are part of the system. • Define service responsibilities, interfaces, and data contracts that evolve safely. • Specify behavior under retries, timeouts, partial failures, and dependency degradation. • Choose consistency and durability guarantees that match risk, latency targets, and operational realities. • Design the request path for predictable tail latency and controlled resource usage. • Build and operate high-performance services and APIs that keep authentication reliable, secure, and fast at scale. • Implement distributed services that are safe under concurrency and robust to duplicate and out-of-order events. • Build real-time scoring and decision services with clear input/output contracts and bounded execution time. • Build distributed training pipelines that scale, are reproducible, and produce auditable artifacts. • Build pipelines that move data and model artifacts through validation, promotion, and release. • Define automated quality gates for service changes and releases. • Add checks for data quality, schema/contract adherence, and training-serving consistency where appropriate. • Define acceptance criteria tied to measurable outcomes and production behavior. • Ship changes with staged rollouts and rollback readiness as defaults. • Coordinate multi-service releases with clear cutover and recovery plans. • Use production signals to validate rollouts and trigger rollback when risk is high. • Instrument the full path with metrics, logs, and traces that enable fast detection and diagnosis. • Implement alerting that reflects user impact, not just component health. • Lead incident response for your services, restore service quickly, and communicate clearly during events. • Run post-incident reviews and close follow-ups that measurably reduce recurrence. • Drive reliability work through SLIs, SLOs, and error budgets, and make tradeoffs explicit. • Improve performance and cost through profiling, load testing, and capacity planning. • Raise engineering quality through reviews, standards, and simplification of operationally expensive designs. • Align across teams on interfaces, data contracts, and reliability expectations to reduce coordination friction. • Evaluate new approaches when they materially improve security, performance, delivery safety, or operational simplicity.