Principal Python Engineer (healthcare solution)

Intellias; Polska; On-site; PLN 180,000 - 260,000; Full time

Job summary

Intellias is building a healthcare data platform with FHIR interoperability and cloud-native infrastructure. The role focuses on designing, prototyping, building, reviewing, and operating production systems, while guiding platform architecture across engineering teams.

You will work on event-driven architectures, API governance, and LLM/AI integration points, mentoring senior engineers and collaborating with US stakeholders to ensure reliability and scalability.

Qualifications

• 8+ years of software engineering experience, with large-scale distributed systems.
• Strong backend skills in Python and production-grade services.
• Experience with REST APIs and GraphQL.
• Cloud-native architecture on AWS; Kubernetes experience.
• Hands-on engineering mindset with architecture knowledge.
• Proven ability to mentor engineers and influence architecture.
• Professional English for collaboration with US-stakeholders.

Responsibilities

• Design and build large-scale, event-driven distributed systems.
• Define and evolve API architecture (REST and federated GraphQL).
• Shape healthcare data architecture with FHIR-based interoperability.
• Collaborate with AI teams on LLM/agent integration points.
• Provide architectural reviews and establish reusable platform patterns.
• Partner with cross-geo teams across engineering, product, data, and security.

Skills

Distributed systems

Python

REST/GraphQL APIs

AWS cloud

Kubernetes

Technical leadership

Mentoring

Data interoperability

Tools

MongoDB

ClickHouse

Databricks

Spark

FHIR

Job description

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.

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.