In 12 months

What You'll Be Doing

Owning complete vertical slices of cross-team or platform-level work — including the multi-quarter migrations, deprecations, and architectural pivots that single-team scope can't carry
Setting the technical bar for what "right" looks like in your domain — sharpening designs, catching architectural drift (including in AI-generated work), and shaping the patterns the team builds toward
Proactively identifying and evaluating platform-level opportunities — new architectures, tools, or capabilities that improve reliability, customer outcomes, or what we can credibly take to market — and driving them to a decision with evidence, alternatives, and tradeoffs
Defining SLOs and observability for what you ship; sharing on-call ownership as a baseline expectation, not an escalation path
Leading the rearchitecture of legacy or high-debt areas where the right call is structural — and seeing the migration through to deprecation
Driving the team's adoption of agentic AI-assisted development — building internal tooling, defining workflows, and setting the standard for what AI-native engineering looks like at Arcadia
Engaging directly with customers and partners during incidents, design reviews, and major rollouts — closing the loop between what we build and what they actually experience
Mentoring Senior Engineers through code review, design partnership, and direct coaching — the trajectory of the engineers you coach is part of how your impact is measured
Influencing engineering practice across teams — architecture standards, operational excellence, AI strategy — through written guidance, working examples, and direct involvement in the hardest problems

What You'll Bring

A track record as the technical owner of cross-team or platform-level initiatives — multiple systems you've taken from ambiguous beginnings through long-term operation, with concrete examples you can walk us through
Deep technical foundations across distributed systems, data infrastructure, and service architecture — you reason from first principles, anticipate failure modes, and recognize architectural drift before it compounds. You are the person teammates escalate structural calls to, and the one who catches when an AI-generated design is plausible but wrong
Proven ability to own complex systems from design through rollout and long-term operation — including the unglamorous middle (observability, on-call, migration paths, deprecations)
A track record of identifying and driving non-obvious technical investments — improvements to the stack, reliability, or platform capability that you spotted, evaluated, and shepherded through the decision
Demonstrable depth with agentic AI-assisted development — actively using agentic coding tools, context-engineered environments, and AI-augmented workflows, with examples of AI-native tooling, custom agents, or team patterns you've built, shipped, or substantially shaped. The work needn't have happened at your current employer — what matters is that the depth is real, the engineering judgment driving it is yours, and you can walk us through both in detail, including specific cases where you caught the agent producing plausible-but-wrong work and corrected course
Track record of mentoring and coaching Senior Engineers to higher levels of judgment and impact — not just answering questions, but visibly raising their bar over time
Strong customer and product orientation — comfortable engaging directly with customers, partners, or downstream consumers to define problems before building and validate outcomes after shipping
Clear written communication — design docs, reviews, and post-release writeups that establish standards beyond your immediate work
Demonstrated influence across team boundaries through technical credibility rather than positional authority

Would Love for You to Have

Experience defining org-wide AI adoption strategy — what was adopted, what was rejected, what got measured, and what the org actually changed about how it builds

Healthcare technology, HIPAA-regulated environments, or other regulated data domain experience

Track record of incubating new product directions or greenfield platform capabilities from ambiguous starting points

Speaking, writing, or open-source presence that reflects broader technical influence

Tech You'll Work With

Arcadia's platform processes petabyte-scale healthcare data through a lakehouse architecture built on open table formats. Apache Spark drives distributed compute; dbt drives transformations; Kafka moves streams; Cassandra/Scylla handles high-throughput storage; OpenSearch / Elasticsearch powers search and indexing — all orchestrated on Kubernetes in AWS, with services spanning TypeScript/Node, Python, Go, and Java/Kotlin (polyglot, by design).

We're actively expanding this entire platform-stack to be AI-native — enabling the creation of custom agents built on Arcadia's data foundation to solve some of healthcare's biggest challenges. Principal Engineers shape the patterns here. Depth in distributed compute, data infrastructure, or platform engineering translates directly.

What You'll Get

The opportunity to own one of Arcadia's most consequential platform domains and to set its direction for years
Direct influence on how Arcadia's engineering org adopts AI — your tooling, patterns, and standards become examples others build on
Competitive compensation, comprehensive benefits, and Flexible Time Off (~22-day company average)
Be a part of a mission-driven company that is transforming the healthcare industry by changing the way patients receive care

About Arcadia

Protect Yourself

Originally posted on Himalayas