Java Architect

Tech Lead – Architect (Java)

Remote · Full-Time

Looking Jaipur or nearby located candidates because need to go one week onsite then remote

About the Role

We’re looking for a Senior Software Engineer who takes ownership seriously, someone who

designs solutions, ships them, and stands behind them in production. You’ll work across a

technically interesting stack on systems that process millions of provider records.

This is a role with real scope: you’ll influence architecture, shape engineering practices, and

work directly with product and leadership to solve hard problems in a domain that genuinely

matters.

Technologies & Tools

Java 21 / Quarkus · React / TypeScript · GCP (Spanner, BigQuery) · Kafka · Docker /

Kubernetes · GitHub Actions · Sentry · REST / GraphQL / gRPC · Cursor / Claude

Code / Codex

Problems You’ll Solve

Healthcare’s provider data problem is a hard distributed systems problem. Hundreds of primary

sources state boards, payers, federal registries each with their own schema, SLA, and failure

mode. Downstream, real credentialing and network decisions depend on whatever truth we can

surface.

API contract stability at velocity. You’re building a platform hundreds of integrations depend

on. How do you evolve a Quarkus/REST API, adding resources, deprecating fields, shifting data

models without breaking consumers? Contract-first design, versioning strategy, and backward

compatibility aren’t theoretical here.

Integration reliability at scale. Upstream sources go down, change schemas, and return dirty

data. You’ll build the patterns that absorb that chaos idempotent consumers, dead-letter queues,

circuit breakers, and reconciliation pipelines on top of Kafka and Spanner.

Entity resolution on messy real-world data. Deduplicating and reconciling provider records

across hundreds of heterogeneous sources, where a wrong merge has downstream

consequences. MDM patterns, confidence scoring, and deterministic vs. probabilistic matching

at scale.

AI-augmented velocity without regression. We use Cursor and Claude Code as force

multipliers. The engineering problem is building review culture, eval frameworks, and test

coverage that keeps quality high as output volume increases.

Observability for a data platform, not just a service. Uptime isn’t enough; you need to know

when a provider record is stale, inconsistent, or wrong. You’ll instrument data quality and

lineage, not just p99 latency.

What We’re Looking For

Engineering fundamentals

* 8+ years building and maintaining production-grade systems including systems where

your API is someone else’s dependency and breaking it has real downstream

consequences

* Track record of shipping high-quality software in fast-paced environments you define the

solution, not just implement a spec

* Strong engineering fundamentals: testing, clean code, maintainability, and performance

optimization

* Experience improving system reliability you’ve debugged hard production problems and

made them not happen again, with SLOs and alerting to prove it

* Comfort mentoring earlier-career engineers and influencing technical direction

API & architecture depth

* Deep experience designing and evolving APIs under active consumers: versioning

strategy, backward compatibility, and contract-first thinking

* Fluency across API paradigms REST, GraphQL, gRPC, and async/event-driven APIs

(webhooks, Kafka topics as contracts) and the judgment to know when each is the right

tool

* Hands-on experience with service-oriented and distributed architectures you’ve worked

across SOA, microservices, and event-driven patterns and can make principled tradeoffs

between them based on coupling, latency, and operational complexity

* Experience designing for API consumers as first-class stakeholders SDK ergonomics,

pagination, rate limiting, error semantics, and documentation as part of the contract, not

an afterthought

* Experience with integration patterns at scale you’ve built or maintained systems that

aggregate and normalize data from many heterogeneous upstream sources, and you

understand the reliability and consistency tradeoffs that come with it: circuit breakers, retry

strategies, idempotency, eventual consistency Data-intensive systems

• Strong data modeling instincts you understand the difference between a schema that’s

easy to write and one that’s easy to query, evolve, and trust at scale

* Experience with high-throughput, event-driven systems: you understand ordering

guarantees, consumer lag, and failure modes in Kafka-like architectures

* Strong sense of data quality: lineage, freshness, and correctness matter as much to you

as throughput

AI-era engineering

* In an AI-augmented engineering environment, you write less and review more you’re

skeptical of generated code in the right ways, and you use that leverage to ship 2–3x

what a non-AI-fluent engineer would

* Fluency with AI-assisted engineering tools (Cursor, Claude Code, MCP servers) this is

part of how we work, not a nice-to-have

Communication & compliance

* Strong written and verbal communication you can explain a technical tradeoff to an

engineer and a product manager in the same conversation

* Experience with sensitive data and security best practices (PII, access controls) in

regulated or compliance-adjacent environments

Nice to Have

* Experience building or operating AI/LLM pipelines in production (not just prototypes)

including eval frameworks, fallback behavior, and monitoring for non-deterministic outputs

* Experience with entity resolution or MDM systems at scale deduplicating messy

real-world data across disparate sources

* Familiarity with healthcare credentialing workflows

* Familiarity with healthcare, compliance, or regulated environments