Agentic AI Architect — Backend Platforms and Voice Agents

Role overview

We are hiring a hands-on architect to design the backend foundations and the shared agent platform

that every team builds products on. You will own architecture across products and client deployments

— service boundaries, data flows, agent execution, real-time voice infrastructure and operational

reliability — and consolidate the systems different teams use today into one platform that is faster and

safer to build on. You will set the engineering practices the whole company follows, and partner with

senior AI engineers on solution delivery and with inference engineers on model-serving interfaces.

Key responsibilities

Backend and distributed systems

• Design service boundaries, APIs, data models, databases, caching and event-driven integrations

that scale to [thousands] of concurrent agent sessions.

• Make consistency, partitioning and replication trade-offs explicit, and design for partial failure

with timeouts, backpressure, circuit breakers and graceful degradation.

• Scale real-time voice infrastructure: session routing, media servers, and horizontal scaling of

WebSocket, SIP and WebRTC connections.

• Set reliability targets (SLOs), latency and cost budgets, and plan capacity across cloud and on-

premises deployments.

• Manage all infrastructure as code with GitOps workflows, so every environment is reproducible,

reviewed and auditable.

• Track model, speech and infrastructure cost per team, product and client, and set up chargeback

so owners see and control their spend.

Agent platform

• Build a shared agent platform: agent runtime, tool registry, enterprise connectors, memory and

retrieval services, and a model gateway with routing, fallbacks, rate limits and caching.

• Architect durable agent execution with persisted state, checkpoints, retries, idempotency,

timeouts, compensation for failed actions and human approval points.

• Enforce tenant isolation, authentication, authorisation, tool permissions and audit trails at the

platform level, so every product inherits them.

• Make the platform self-serve: SDKs, templates and documentation that let a team launch a new

voice or chat agent in days, not weeks.

• Run an internal developer portal (such as Backstage) as the single place to find services,

connectors, templates, owners and docs.

• Provide shared evaluation, tracing and monitoring services that every agent uses by default.

• Build a data platform for call and agent analytics: transcripts, outcomes, tool usage and latency,

feeding product insights, evals and client reporting.

Consolidation and company-wide practices

• Audit existing agent and voice stacks, and consolidate duplicate pipelines, connectors and

prompt setups onto the platform with migration plans that keep live clients running.

• Define reference architectures and golden paths for common use cases, such as voice agents,

chat agents and document workflows.

• Set engineering standards: architecture decision records, design reviews, API guidelines, testing

and eval requirements, release controls and incident management.

• Maintain a tech radar of approved, trial and retired tools, and lead build-versus-buy decisions

for agent frameworks, model providers, speech vendors and infrastructure.

Security, governance and resilience

• Design systems to meet security and compliance standards such as SOC 2, ISO 27001 and India's

DPDP Act, and support audits with evidence from the platform.

• Set AI governance policy: red-teaming of agents before launch, model risk reviews, prompt-

injection defences and rules for which data each model may see.

• Define disaster recovery targets (RPO and RTO), run regular recovery drills and use chaos testing

to prove agents and voice sessions survive failures.

Technical leadership

• Translate business workflows into technical designs, build reference implementations and

review critical code.

• Guide engineering teams through architectural decisions, and mentor senior engineers to make

sound design choices on their own.

• Manage a team of [N] engineers: set goals, run one-on-ones and performance reviews, and

support their career growth.

• Act as solution architect in pre-sales: scope client requirements, design proposed solutions,

estimate effort and present the architecture to client technical teams.

• Align product, infrastructure, inference and delivery teams around one implementable

architecture, and represent it to enterprise clients.

Required qualifications and skills

• Strong backend and distributed-systems experience, including ownership of production

architecture and its evolution under growing scale.

• Hands-on Python expertise and deep knowledge of APIs, SQL and NoSQL databases, queues,

caching, concurrency and consistency trade-offs.

• Practical experience delivering LLM agents with tool integrations, stateful orchestration,

retrieval and failure handling.

• Understanding of real-time, low-latency systems such as voice or streaming, including

backpressure and connection scaling.

• Experience building internal platforms or shared services adopted by multiple teams.

• A track record of leading system consolidations or architectural migrations without disrupting

production.

• Ability to design for partial failures, long-running tasks and secure multi-tenant execution,

supported by automated tests, tracing and deployment practices.

• Ability to write clear design documents, explain trade-offs and align product, infrastructure and

delivery teams.

• Experience managing engineers, and working directly with clients in pre-sales or solution design.

• A relevant technical degree or equivalent experience.

Preferred experience

• Workflow engines and agent orchestration frameworks such as Temporal or LangGraph, model

gateways, and vector search.

• Additional backend languages such as Go or Java.

• Kubernetes, service meshes and infrastructure as code.

• Production voice systems at scale: SIP, media servers, LiveKit or similar.

• Multi-tenant SaaS platforms and enterprise deployments with restricted infrastructure.

• Model-serving interfaces such as vLLM or Triton, and working with inference teams on

throughput and cost.

• Multimodal agents that combine voice, text and vision.

Success in the role

Success means one reliable platform that every team builds on, with clear architectural decisions and

measurable control of task completion, latency and cost. In your first year, that looks like:

• Platform adoption: most new agents are built on the shared platform and found through the

developer portal, and the time to launch a new agent drops from weeks to days.

• Consolidation: duplicate pipelines and connectors are retired, with no disruption to live clients.

• Scale and reliability: SLOs hold as traffic grows, agent runs recover cleanly from partial failures,

and recovery drills meet their targets.

• Cost control: every team and client can see its model and infrastructure spend, and per-task

cost stays within budget.

• Security and governance: compliance audits pass, and every agent is red-teamed and reviewed

before launch.

• Company-wide practices: design reviews, decision records, eval gates and release controls are

used across teams.

• Team and clients: your team grows in skill and ownership, and clients sign off on architectures

you designed in pre-sales.

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