Software Engineer Architect – AI Agents (Healthcare)

Role Summary

Key Responsibilities

1. Own the end-to-end architecture of the AI agents platform — services, APIs, data models, async task pipelines, LLM/voice agent orchestration, and integrations with payer systems, EMRs, and telephony.
2. Design for reliability, latency, and safety: define patterns for fault tolerance, idempotency, retries, observability, and graceful degradation across agent workflows that run at scale in production.
3. Set the architecture for multi-tenancy, data isolation, and PHI handling in partnership with our Security and Compliance Lead, keeping HIPAA and SOC 2 requirements designed in rather than bolted on.
4. Make and document high-leverage technical decisions — build vs. buy, service boundaries, storage and queueing choices, LLM provider and inference strategy — through lightweight design reviews and ADRs.
5. Stay hands-on: prototype critical paths, write production code on the most ambiguous and highest-risk problems, and unblock teams when they hit hard technical walls.
6. Partner with the platform team on how workloads run on our Kubernetes (GKE) infrastructure — autoscaling, deployment topology, cost, and SLOs for agent and voice workloads.
7. Raise the engineering bar: mentor senior engineers, drive code and design review culture, and define standards for testing, evaluation, and release safety across teams.
8. Translate product and customer needs into a technical roadmap; work with founders and product to sequence architectural investments against business priorities.
9. De-risk scale: anticipate where the system breaks at 10x call volume, 10x customers, and 10x team size, and lay the groundwork before it does.

Requirements

1. 10+ years of professional software engineering experience, including several years designing and operating large-scale, production distributed systems.
2. A track record of owning architecture for a product or platform end to end — you have made foundational technical decisions, lived with their consequences, and evolved them.
3. Deep expertise in backend systems: service design, RESTful/async APIs, event-driven architectures, task queues, and data modelling across relational and non-relational stores.
4. Strong grasp of reliability engineering — observability, fault tolerance, capacity planning, and incident-informed design — for systems with strict correctness and latency requirements.
5. Hands-on proficiency in at least one of our core languages (Python or Go); comfort reading and reviewing code across the stack, including React-based frontends.
6. Working knowledge of cloud-native infrastructure (GCP/AWS/Azure), Kubernetes, and infrastructure-as-code, and how architectural choices play out operationally.
7. Experience building or integrating AI/LLM-backed systems — or clear evidence you can go deep fast on agent orchestration, evaluation, guardrails, and inference trade-offs.
8. Strong security instincts: you design with data isolation, encryption, access control, and auditability in mind from day one.
9. Excellent written and verbal communication — you can carry a design from whiteboard to ADR to aligned teams, and explain trade-offs to founders and customers alike.
10. Ability to tackle complex, ambiguous technical problems and drive them to crisp decisions.

Nice-to-Haves

Why This Role Matters

Culture & Growth

1. 100ms is an engineering-first startup. Our team includes former entrepreneurs, AI engineering specialists, and healthcare operations professionals, with experience at major technology companies around the world.
2. You can grow as a senior individual contributor or into technical leadership — you set your own trajectory, with direct access to the founders.
3. We believe in-person collaboration builds better systems and stronger culture: employees work from our Bengaluru office at least three days a week — Tuesday, Wednesday, and Friday. Some overlap with U.S. time zones is expected for customer and partner engagement.