Principal Python Engineer (healthcare solution)

Intellias is looking for a Principle Python Engineer in Platform Architect team to join a healthcare technology initiative focused on building a modern, scalable platform for connected health data and digital healthcare experiences. This is a senior hands-on engineering role for someone who combines deep distributed-systems expertise, strong backend engineering skills, and architectural thinking. You will design and build platform capabilities used across multiple engineering teams, establish reusable architectural patterns, and help evolve a cloud-native healthcare platform operating at scale. The project is building a FHIR-based healthcare data platform that integrates data from EHRs, digital health applications, wearables, portals, and other healthcare data sources into a unified ecosystem. The platform operates at the intersection of health data interoperability, distributed systems, API platforms, cloud infrastructure, and AI-enabled healthcare experiences. Its architecture includes event-driven processing, federated GraphQL and REST APIs, FHIR services, high-volume data storage and analytics, and integration points for LLM- and agent-based capabilities. You will work horizontally across engineering teams rather than within a single product squad. The focus is on solving complex platform-level problems around scale, reliability, data consistency, interoperability, API governance, and production architecture. This is not an architecture-only position. You will be expected to design, prototype, build, review, and operate production systems, staying close to both the code and real-world system behavior. What you'll do: - Design and build large-scale, event-driven distributed systems, including orchestrators, worker services, asynchronous processing pipelines, schema governance, and reliable consumer patterns. - Define and implement platform architecture around Kafka, including topic and partition design, schema evolution, idempotency, consumer groups, retry strategies, and production failure handling. - Own and evolve API architecture across REST and federated GraphQL, establishing scalable patterns for schema composition, versioning, contract evolution, and API governance. - Design and build reusable platform capabilities that enable engineering teams to onboard new services and data sources with minimal custom implementation. - Contribute directly to production code, prototypes, reference implementations, and technical proofs of concept. - Partner with AI engineering teams to define reliable integration points for LLM- and agent-based capabilities, including retrieval, evaluation, guardrails, observability, latency, cost, and data-protection boundaries. - Shape healthcare data architecture, including FHIR-based interoperability, terminology services, data-quality controls, high-volume storage, and analytical workloads. - Drive architecture and technical design reviews, documenting key decisions, trade-offs, standards, and reusable engineering patterns. - Use production telemetry, observability, and operational data to validate architectural decisions and troubleshoot complex distributed-system behavior. - Establish architectural patterns for security, identity, multi-tenancy, scalability, resilience, and protected health data. - Mentor senior engineers and technical leads in system design, distributed-systems engineering, and architectural decision-making. - Collaborate closely with U.S.-based engineering, product, data, security, and AI stakeholders while working effectively across geographically distributed teams. - Promote an AI-enabled engineering approach, using modern AI development tools where they improve engineering velocity, quality, analysis, documentation, testing, or architectural exploration. What we're looking for: - 8+ years of software engineering experience, with significant experience designing and operating large-scale distributed systems. - Strong hands-on backend engineering skills in Python and/or another modern backend language, with the ability to work effectively across a polyglot environment and solid experience building production-grade services and APIs. - Proven experience designing event-driven architectures, ideally using Apache Kafka or comparable distributed messaging technologies. - Deep understanding of distributed-system fundamentals, including partitioning, idempotency, back-pressure, eventual consistency, failure recovery, and consistency/availability trade-offs. - Strong experience designing and evolving REST APIs and GraphQL-based architectures, including schema design, versioning, composition, and contract governance. - Strong system-design skills and demonstrated ownership of complex systems from architecture through implementation and production operation. - Experience with cloud-native architectures on AWS, including Kubernetes-based environments, observability, scalability, resilience, and production operations. - Strong data architecture fundamentals: data modeling, storage strategies, query optimization, and designing systems for high-volume transactional and analytical workloads. - Practical experience with modern data and storage technologies such as MongoDB, ClickHouse, Databricks, Spark, or comparable platforms. - Understanding of identity and security concepts such as OIDC, OAuth/token exchange, authorization, multi-tenancy, and data isolation. - Ability to work effectively across a polyglot technology environment and make architectural decisions based on system requirements rather than technology preference. - Strong communication and technical leadership skills, including the ability to drive architecture discussions, technical design reviews, and engineering standards across multiple teams. - Strong hands-on engineering mindset — able and willing to build production code, prototypes, reference implementations, and tooling rather than operating exclusively at an architectural level. - Professional English sufficient for direct collaboration with U.S.-based engineering and product stakeholders. Nice to have: - Experience with FHIR, HL7, EHR integrations, healthcare interoperability, or digital health platforms. - Experience designing systems handling PHI/PII or other regulated and sensitive data. - Familiarity with healthcare security and compliance requirements, including HIPAA-related architectural considerations. - Experience with Java/Spring Boot, TypeScript/Node.js, or other languages used in large-scale backend ecosystems. - Experience with federated GraphQL platforms such as WunderGraph Cosmo, Apollo Federation, or similar technologies. - Practical experience integrating LLMs or AI agents into production systems, including retrieval, evaluation, guardrails, observability, latency, and cost considerations. - Experience with AI-assisted software engineering tools such as Claude Code, GitHub Copilot, Cursor, or similar tools, and an ability to use them effectively throughout the engineering lifecycle. - Experience establishing reusable platform patterns, architecture standards, or internal developer platforms used by multiple engineering teams. - Previous Staff/Principal-level experience mentoring senior engineers and influencing architecture beyond a single team. Why join us? This role provides an opportunity to influence the core architecture of a modern healthcare technology platform, rather than owning only a single application or service. You will work on technically demanding problems involving distributed systems, event-driven architecture, healthcare interoperability, API platforms, cloud infrastructure, large-scale data processing, and emerging AI capabilities — while remaining directly involved in engineering and production systems. The position offers Staff-level technical influence across multiple teams, with the ability to establish architectural patterns that become building blocks for the broader engineering organization. Most importantly, this is a role for an engineer who wants to combine architecture with hands-on implementation: designing systems, proving architectural decisions through code, operating them in production, and helping engineering teams build faster on top of reliable, reusable platform foundations.