Agentic AI Architect — Backend Platforms and Voice Agents

YAL; Hyderabad; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

YAL in Hyderabad is seeking an Agentic AI Architect for Backend Platforms and Voice Agents with 8+ years of experience to own architecture across products and client deployments.

You will shape the shared platform, set reliability targets, lead engineering practices, and collaborate with AI and inference teams to deliver scalable, secure voice and chat agent solutions.

Qualifications

• Strong backend and distributed-systems experience with production ownership.
• Hands-on Python expertise and deep knowledge of APIs, SQL/NoSQL, queues, caching, concurrency.
• Experience delivering LLM agents with tool integrations, stateful orchestration and retrieval.
• Understanding of real-time, low-latency systems such as voice/streaming.
• Experience building internal platforms used by multiple teams.
• Track record leading consolidations or architectural migrations without disruption.
• Design for partial failures and secure multi-tenant execution with tests, tracing and deployment.
• Ability to write clear design documents and explain trade-offs.
• Experience managing engineers and engaging with clients in pre-sales.
• A relevant technical degree or equivalent experience.

Responsibilities

• Design service boundaries, APIs, data models and databases at scale.
• Scale real-time voice infrastructure, including WebSocket, SIP and WebRTC connections.
• Set reliability targets (SLOs) and plan capacity across cloud/on-prem environments.
• Manage infrastructure as code with GitOps for reproducible environments.
• Build a shared agent platform with runtime, tool registry and model gateway.
• Enforce multi-tenant isolation, authentication and audit trails at platform level.
• Make the platform self-serve with SDKs, templates and documentation.
• Lead internal developer portal usage (Backstage-like) across teams.
• Provide monitoring, tracing and cost visibility for agents and platforms.
• Develop data platform for transcripts, analytics and client reporting.

Skills

Backend architecture

Python

APIs

SQL/NoSQL databases

LLM agent integration

Real-time systems

Cloud & on-prem capacity

Leadership & mentoring

Education

Relevant technical degree

Tools

Kubernetes

Temporal

LangGraph

LiveKit

SIP/WebRTC

Job description

Agentic AI Architect — Backend Platforms and Voice Agents

8+ years

Hyderabad office

Full-Time

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- 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.
• 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. 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