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
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.
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.
Full description
About the role
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 , including retrieval, evaluation, guardrails, observability, latency, cost, and data-protection boundaries.
Principle Python Engineer (healthcare solution) at Intellias (Remote) — aijobsearch
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.
LLM- and agent-based capabilities
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 you'll get
At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth.
We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law.
We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.
What you need for this
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.