Senior Python Developer - Autonomous AI agents

Role overview

The client is building a multimodal AI platform that processes multi-hour video, audio and text to generate structured insights, narratives and highlight workflows for broadcasters and media organisations.

We are seeking a Backend / Platform Engineer to design and build high-throughput media pipelines, robust APIs, and model-serving infrastructure that connect our AI engine (video perception + multimodal reasoning) to real products and customer environments.

This is not a CRUD‑only backend role.

You will work on:

• long‑running jobs
• distributed processing
• GPU inference orchestration
• storage for embeddings and metadata
• integration with AI models
• reliability and observability at scale

Key responsibilities

Media ingestion & processing pipelines

• Design and implement ingestion pipelines for multi‑hour video and audio content.
• Build microservices for frame extraction, audio processing, transcription integration and metadata generation.
• Handle long‑running, asynchronous jobs using queues, workers and robust retry strategies.
• Integrate with FFmpeg or similar tools for transcoding, segmenting and preparing media for AI models.

API & platform architecture

• Design and implement REST/gRPC APIs that expose AI model outputs (perception, multimodal alignment, narratives) to frontend and external systems.
• Define clear contracts for internal services and external integrations.
• Implement authentication, authorisation and rate‑limiting for platform endpoints.
• Ensure backward‑compatible API evolution as the product matures.

Model‑serving & AI integration

• Integrate with AI inference services (video models, multimodal models, LLM/VLM) running on GPUs or specialised infrastructure.
• Design request/response flows that handle large payloads, streaming outputs and structured results.
• Optimise throughput and latency for inference pipelines, including batching, caching and concurrency control.
• Collaborate closely with AI engineers to productionise models and debug end‑to‑end behaviour.

Storage, data models & performance

• Design data models to store embeddings, timelines, metadata, scene/shot boundaries, and narrative units.
• Work with appropriate storage technologies (SQL/NoSQL, object storage, search indices) based on access patterns.
• Implement indexing and query strategies for fast retrieval of segments, highlights and multimodal insights.
• Optimise performance for large datasets and high‑volume workloads.

Reliability, observability & operations

• Implement logging, metrics and tracing across services for debugging and monitoring.
• Set up health checks, circuit breakers and graceful degradation for critical services.
• Work with CI/CD pipelines to ensure safe, repeatable deployments.
• Collaborate on Kubernetes‑based deployments (or equivalent orchestration) for scaling services.

Requirements (must‑have)

Experience:

• 4–8 years in backend or platform engineering.
• At least 3 years working on distributed systems, high‑throughput services or complex pipelines (not just simple CRUD apps).

Languages & frameworks:

• Strong proficiency in Python or Node.js (one primary, both are a plus).
• Experience with at least one modern backend framework (FastAPI, Flask, Express, NestJS, etc.).

Distributed systems & pipelines:

• Hands‑on experience with queues and workers (e.g. Celery, RabbitMQ, Kafka, SQS, etc.).
• Experience building asynchronous, long‑running job pipelines.
• Understanding of idempotency, retries, backoff, and failure handling.

APIs & integration:

• Strong experience designing and implementing REST APIs (gRPC is a plus).
• Experience integrating with external services and handling network‑level failures.

Cloud & infrastructure:

• Experience deploying services on AWS, GCP or Azure (EC2/Compute Engine, S3/GCS, IAM, networking basics).
• Experience with Docker; exposure to Kubernetes is a strong plus.

Data & storage:

• Experience with SQL and at least one NoSQL store.
• Ability to design schemas and data models for performance and maintainability.

Engineering quality:

• Strong debugging skills across services and environments.
• Experience with unit/integration tests for backend systems.
• Clear, structured communication in English.

Nice‑to‑have

• Experience with media/video processing (FFmpeg, transcoding, segmenting).
• Experience with AI/ML model integration (serving models, handling inference requests).
• Experience with search/retrieval systems (e.g. Elasticsearch, vector databases).
• Experience with observability stacks (Prometheus, Grafana, OpenTelemetry).
• Experience working with remote teams across time zones.

What we are explicitly NOT looking for

To reduce noise and mismatches, we are not looking for:

• Pure CRUD‑only backend developers with no pipeline or distributed systems experience.
• Engineers who have only worked on small, single‑service apps without scale or complexity.
• Candidates who cannot explain trade‑offs in architecture, data modelling and reliability.
• Candidates who are uncomfortable with ownership of subsystems end‑to‑end.

Why join us

• Work on real, complex problems at the intersection of media, AI and distributed systems.
• Collaborate with senior AI engineers working on perception, multimodal fusion and narrative reasoning.
• Build the core platform that turns AI models into a usable product for broadcasters and media organisations.
• Operate with high ownership, clear expectations and direct access to the CTO.

Job Overview

Architect and build scalable, high-performance backend systems while working on mission-critical platforms that process real-time market data and portfolio analytics. The role also involves leveraging Generative AI capabilities to enhance data intelligence, automation, and user-facing features, while ensuring regulatory compliance and secure financial transactions.

Key Responsibilities

• Design, develop, and maintain scalable backend services and APIs using NodeJS and Python
• Build event-driven architectures using RabbitMQ and Kafka for real-time data processing
• Develop and manage data pipelines integrating PostgreSQL and BigQuery for analytics and warehousing
• Integrate and deploy Generative AI models (LLMs, embeddings, AI APIs) into backend systems for automation, insights, and intelligent workflows
• Design AI-powered features such as recommendation systems, document processing, or conversational interfaces
• Ensure system reliability, security, and low-latency performance for mission-critical systems
• Lead technical design discussions, conduct code reviews, and mentor junior engineers
• Optimize database queries, implement caching strategies, and improve overall system performance
• Collaborate with cross-functional teams to deliver end-to-end product features
• Implement monitoring, logging, and observability solutions

Required Skills and Qualifications

• 2+ years of professional backend development experience
• Strong expertise in NodeJS and Python for production-grade applications
• Proven experience building RESTful APIs and microservices architectures
• Experience working with Generative AI frameworks/APIs (OpenAI, LangChain, vector databases, prompt engineering)
• Understanding of integrating LLMs into production systems (RAG, embeddings, fine-tuning basics)
• Strong proficiency in PostgreSQL, including query optimization and schema design
• Hands-on experience with RabbitMQ and Kafka
• Experience with BigQuery or similar data warehousing solutions
• Solid understanding of distributed systems, scalability patterns, and high-traffic applications
• Strong knowledge of authentication, authorization, and security best practices
• Experience with Git, CI/CD pipelines, and modern development workflows
• Excellent problem-solving and debugging skills
• Exposure to fintech or financial services, cloud platforms (GCP/AWS/Azure), Docker/Kubernetes, caching tools (Redis/Memcached), and regulatory requirements (KYC, compliance, data privacy) is a plus

Apply directly at: https://wohlig.keka.com/careers/jobdetails/136351

Most sales tools help you send emails. We’re building something different.

At Salesforge, we’re creating autonomous AI agents that can:

Find the right prospects

Generate highly personalized outreach

Run conversations

And book meetings

All without human involvement.

Why this is interesting

A lot of AI products stop at “generate text.” We’re focused on outcomes.

That means solving problems like:

How do you generate messages that actually get replies?

How do you evaluate and improve agent performance over time?

How do you orchestrate millions of AI-driven interactions reliably?

How do you combine structured data + LLMs in a way that scales?

If you enjoy working at the intersection of systems + AI + real-world feedback loops, this will feel like a playground.

What you’ll be working on

You won’t be maintaining legacy systems.

You’ll be:

Designing and building core backend systems that power our AI agents

Creating APIs and services that handle high-scale, real-time workflows

Working with queues (Kafka / SQS / RabbitMQ) to orchestrate async systems

Thinking deeply about performance, cost, and reliability in AI pipelines

Shipping features end-to-end with a small, senior team

The team

We’re a small group of experienced builders. We move quickly, care about quality, and avoid unnecessary process.

No layers of management.

No long planning cycles.

Lots of ownership and autonomy.

What we’re looking for

5+ years of backend engineering experience

Strong system design fundamentals

Experience with distributed systems and async processing

Familiarity with relational and/or document databases

Clear communicator, low ego, high ownership

Why join

You’ll work on a product where the output is measurable (meetings booked, revenue generated)

You’ll have real ownership from day one

You’ll be early in building a new category (AI sales agents)

You’ll grow as fast as we do

Experience: 8+ years, senior candidates only | Type: Full-time | Location: Remote (India)

 ---

 WHAT WE'RE BUILDING

 See http://www.juliet.space

 We're building Juliet, an AI that runs marketing end to end. Our users are marketers, founders, CEOs, growth leads, agencies, and SMBs — not developers. They

 tell Juliet the goal. She plans, writes production code, and ships real marketing: conversion-optimized websites, launch assets, campaigns, audits, autonomously.

 That's the engineering problem in one line: the humans in the loop can't read code, so the agent has to get it right on her own — plan, build, self-correct,

 recover, ship.

 Under the hood: a browser-based studio backed by cloud sandboxes, a real-time SSE streaming pipeline, and a LangGraph agent working across 83 tools and 63 skill

 modules. The agent isn't bolted onto the product. She is the product.

 Small team, big ambitions. You'll ship things users touch daily, not write tickets about them.

 ---

 THE ROLE

 We're hiring one architect-level backend engineer to own Juliet's agentic infrastructure end to end. That means the agent graph, the execution environment, the

 streaming pipeline, the state and memory systems — and setting technical direction for the engineers working alongside you.

 This is a player-coach seat. You'll still write code every day, and your architectural calls become the product. You'll work directly with the founder. No PMs in

 between.

 Frontend is part of the system. You won't be leading it, but you'll need to understand how the agent's output reaches the browser and be able to ship full-stack

 features when needed.

 ---

 THE STACK

 AI agent (primary): Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers

 API (primary): NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events

 Infra (primary): Modal cloud sandboxes, Docker, Netlify deployments

 Frontend (secondary): Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js

 Monorepo: Turborepo, pnpm

 ---

 WHAT YOU'LL WORK ON

 The majority of your time is here:

 Agentic AI workflows — Design, extend, and harden the LangGraph agent graph: multi-step planning, code generation, tool dispatch, self-correction, and recovery

 across 83 tools and 63 skill modules. This is the core of the product.

 Real-time streaming architecture — The SSE pipeline that carries every agent action from the Python backend through NestJS to the browser: event framing,

 reconnection, health monitoring, interrupt handling for plan approvals and clarifying questions.

 Agent execution environments — Sandbox lifecycle on Modal: container spin-up, file sync, terminal I/O, command execution, and live preview with per-asset esbuild

 bundling. The agent lives here.

 State and memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), conversation summarization. How

 the agent knows what it knows.

 Backend API and data layer — NestJS services, Supabase schema, Redis caching, quota enforcement, webhook handling. The plumbing the agent depends on.

 Marketing intelligence pipelines — AEO, CRO, and brand-perception audit engines: multi-LLM probing, parallel inference, streamed structured reports, result

 caching. Audit-at-scale infrastructure.

 The remaining ~25% of your time:

 Full-stack product features — Collaboration (roles and permissions), the Netlify deployment pipeline, subscription and quota flows, onboarding. You'll ship these

 end to end — backend first, frontend to close the loop.

 ---

 WHAT WE'RE LOOKING FOR

 Must-have:

 - 8+ years of professional software engineering, including meaningful time as a tech lead or systems architect who owned something end to end. Closer to ten is

 the norm for people who thrive here.

 - Both worlds on your resume: engineering rigor inside a large company and 0-to-1 ownership at an early-stage startup.

 - Production agentic systems experience. You've built and operated LLM agent systems in production with LangGraph, LangChain, or equivalent — agent graphs, tool

 use, state management, prompt engineering, evals. This means well beyond calling a chat endpoint.

 - Strong Python. You design and ship production Python daily. The agent codebase is yours to own.

 - Architect-level system design. You can own how data flows across four services, make tradeoffs under uncertainty, and defend every call.

 - AI-native development workflow. You drive Claude Code, Codex, or similar agentic tools as everyday instruments — not occasionally. You have opinions about

 working with coding agents because you do it constantly.

 - Real-time backend systems. You've built SSE, WebSocket, or streaming API infrastructure in production — not just consumed it.

 - Strong TypeScript. The API layer and most product features are in TypeScript. You're productive in it.

 Strong plus:

 - Background in developer tools, IDEs, or coding/execution platforms

 - Container runtimes and sandboxed execution (Modal, E2B, Firecracker, or similar)

 - Depth in PostgreSQL, Redis, and Supabase

 - LLM observability and evals tooling (LangSmith or similar)

 - NestJS or equivalent Node.js API framework experience

 - React/Next.js — enough to ship a full-stack feature without handoff

 - Exposure to marketing, growth, or publisher-facing products

 ---

 WHY THIS ROLE IS DIFFERENT

 You own the architecture. Not a feature factory. Not someone else's design doc. The technical execution of an AI product is yours to lead.

 The agent is the product. You're not adding AI to an existing system. You're building and operating the system that is the AI. Every architectural decision

 touches what Juliet can and can't do.

 Hard problems, always. The system spans cloud sandboxes, streaming infrastructure, multi-step agent graphs, and a full-stack web product — for non-technical

 users who can't course-correct a broken output. The bar is high.

 Small team, real leverage. Your code ships to users the same week. No layers of approval.

 ---

 HOW TO APPLY

 Send us:

 1. A short note on the most complex agentic system you've shipped: what broke, and what you'd redo. A link to something you've built that involves agent graphs, tool use, or autonomous multi-step execution

 2. What is one thing you would improve about Juliet? It could be a feature or a bug.

About the Company

The client is revolutionising the way businesses operate through cutting-edge technological solutions. Their focus is on developing intelligent agents and agentic workflows that automate processes and eliminate the need for human effort wherever possible. By leveraging

advanced AI and machine learning, they create systems that enhance productivity and drive efficiency.

Their expertise extends to the fintech, healthcare and medical technology sectors, where they develop innovative solutions that improve patient outcomes and streamline medical operations.

From medical devices to healthcare platforms, their work sits at the intersection of technology and medicine, pushing the boundaries of what's possible. The team is dedicated to continuous learning and growth, ensuring the team members are always at the forefront of the tech landscape.

About the Role

This is a senior, hands-on engineering role at the heart of our product team. You will be one of the most technical people in the room — setting the architecture for our real-time voice AI

agents and building the hardest parts of it yourself. From the systems that power live conversations to the interfaces our clients rely on, you will own how the product is engineered end to end.

We are looking for a genuine lead full-stack engineer with the depth to make architecture decisions that hold up as we scale, and the appetite to still be in the code every day. You should be as comfortable designing the backend services behind a live voice agent as you are shaping a clean interface on top of them — and comfortable being the person others turn to when something is hard.

You will work directly with the founder and product leadership on a fast-moving product, with real influence over technical direction. This is a role for someone who wants ownership at the level of "how the whole thing is built," not just individual features — and who raises the bar for

everyone around them.

What You'll Own

Set the technical direction

• Own the architecture of our core systems — the real-time voice agents, backend
• services, data and APIs — making the decisions that keep the product fast, reliable and scalable as it grows.
• Lead the hardest engineering problems and solve them personally.
• Establish engineering standards — code quality, review practices, testing and technical patterns that the team builds to.
• Drive technical strategy with the founder and product leadership — shaping the roadmap, flagging risk early, and turning product ambition into a sound technical plan.

Build the product end to end

• Design, build and ship features across the stack — backend services, APIs and front-ends — owning them from idea to production.
• Build the client-facing surfaces — dashboards, review tools and configuration interfaces that let our clients run and trust the product.
• Design and evolve the data models and APIs that hold up as we scale across clients.

Make it reliable and fast

• Own production quality — put the monitoring and alerting in place so issues are caught before clients feel them, and performance stays within target.
• Care about performance — find and fix bottlenecks across the stack.
• Build for correctness — put the testing and evaluation in place that keeps the product behaving predictably as it changes.

Lead through the team

• Mentor and grow engineers — through code review, pairing, and setting a technical example others learn from.
• Multiply the team's output — unblock others and lift the overall quality of the codebase.
• Take features from ambiguity to done — turn a rough product goal into a shipped, working capability with minimal hand-holding, and help others do the same.

What We're Looking For

• 8+ years of professional software engineering experience, with significant depth across backend and a track record of owning systems, not just features.
• Strong backend engineering, ideally in Python — building and scaling production services and APIs..
• Proven architecture and system-design ability — you have designed systems that scaled, and can reason clearly about trade-offs.
• Solid fundamentals across APIs, databases and cloud infrastructure.
• Experience building real-time and/or AI-powered products — or clear, demonstrable ability to lead in this area.
• A history of technical leadership — setting standards, mentoring engineers, and being trusted with the hardest problems — while remaining hands-on.
• Excellent communication and a genuine ownership mindset — someone who can be handed an ambiguous, high-stakes problem and be trusted to see it through.

Nice to Have

• Experience working with AI / large language models in production.
• Experience with voice or other real-time products.
• Exposure to healthcare, fintech, or other regulated / high-stakes domains.
• Experience as an early or senior engineer in a startup, where you set direction and wore many hats.

About the Role

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies. We are hiring a Backend Engineer for a dedicated client engagement building an AI-powered application builder platform.

The backend is the operational core of the product: it manages user projects and sessions, coordinates long-running AI agent workloads, maintains project state, and serves as the integration layer between the frontend, the AI system, and the underlying infrastructure.

The mandatory requirement is hands-on production experience shipping Node.js services, with end-to-end ownership of API design, data modelling, and at least one production system involving background job processing or event-driven patterns.

Responsibilities

API and service development: Design and build REST APIs in Node.js with TypeScript. Cover authentication, session management, input validation, structured error handling, streaming responses (SSE, WebSockets), and rate limiting. Maintain clean API contracts that the frontend and AI system can rely on.

Database design and management: Own PostgreSQL schema design for product domains including user accounts, projects, file trees, session state, and generated artefacts. Write efficient queries, manage migrations, and optimise for read patterns that serve a real-time editor experience.

Caching strategy: Implement and maintain caching with Redis for session data, project state, and frequently read configuration. Design cache invalidation logic that keeps the editor experience consistent without stale reads.

Queue and background job management: Implement and operate background job infrastructure using BullMQ or equivalent. AI agent runs are long-running and stateful; handle retries, failure states, priority queues, and concurrency limits.

AI system integration: Build the integration layer between the backend and the AI agent system. Manage job dispatch, result handling, streaming output to the frontend, and error propagation.

Multi-tenancy and access control: Implement tenant data isolation, RBAC, and resource ownership enforcement across all API surfaces.

Observability and reliability: Instrument services with structured logging, metrics, and tracing. Write defensive code with sensible timeouts, fallback behaviour, and circuit breaking on external dependencies.

Testing and code quality: Write unit and integration tests for the services you ship. Review the work of peers and contribute to shared engineering conventions.

Requirements

• Hands-on production Node.js experience (mandatory) — must have personally shipped at least one feature area end to end in a production Node.js service, owning API design, data modelling, and testing.

• 3 to 5 years of professional backend engineering experience. Candidates with slightly less time but strong demonstrated ownership are welcome to apply.

• Strong Node.js and TypeScript. Production experience with Express, NestJS, or Fastify. Solid with async patterns, streaming, error handling, and building services that run reliably under sustained load.

• PostgreSQL depth. Schema design, query writing, indexing, and migrations on at least one production system.

• Redis and caching. Production experience using Redis for caching and session management. Understands cache invalidation trade-offs.

• Queue and background job systems. Hands-on with BullMQ, RabbitMQ, SQS, or equivalent. Experience managing retries, dead-letter queues, job priority, and concurrency control.

• AWS working knowledge. Comfortable with EC2, S3, RDS, SQS, and IAM. Familiar with Docker and basic deployment and environment management.

• Strong written and spoken English. Able to communicate clearly with engineers across disciplines and write precise technical documentation.

Nice to Have

Experience integrating with AI or LLM services (streaming responses, structured outputs, retry patterns); WebSocket or SSE implementation for real-time features; multi-tenant SaaS product experience; GraphQL; OpenTelemetry instrumentation; prior work on developer tools or editor-style products.

About the Role

Shava Studios is building AI-native software products. We are hiring a Mid Senior Full-Stack Engineer to own features end-to-end across the frontend and backend, with depth in system architecture and the AI and real-time systems that connect them. We hire for demonstrated ability rather than years of experience.

Technical Environment

1. Frontend: Next.js (App Router), React, TypeScript, Tailwind CSS, MUI, Radix UI, TanStack Query, Zustand, NextAuth; Remotion for in-browser video editing and rendering.
2. Backend: Python, FastAPI, SQLAlchemy with PostgreSQL and Alembic, Pydantic; hexagonal (ports-and-adapters) microservices.
3. Async & Real-Time: Celery with RabbitMQ, Redis and Redis Streams, server-sent events, and WebSockets.
4. AI: LangGraph, deepagents, and LangChain for agent orchestration across multiple GenAI providers (OpenAI, Google, and others).
5. Tooling & Quality: Vitest, Playwright, Testing Library, pytest, Hypothesis; ruff and mypy; TDD with a strict line-and-branch coverage gate; Docker; CI on Azure Pipelines.

Responsibilities

1. Design, build, and ship full-stack features end-to-end across the UI, backend services, and async job pipelines.
2. Design and evolve service APIs, data models, and database schemas with migrations.
3. Architect and integrate LLM/agent and other AI capabilities behind clean, provider-agnostic abstractions.
4. Build and harden real-time systems (WebSockets, server-sent events, streaming) with reliable delivery and reconnection semantics.
5. Design asynchronous processing pipelines: queuing, workers, retries, idempotency, and failure recovery.
6. Manage third-party integrations (payments, AI providers, storage) behind well-defined interfaces.
7. Optimize frontend and backend performance, state management, and scalability.
8. Own security-sensitive concerns: authentication, authorization, secret handling, and secure service-to-service communication.
9. Instrument observability: logging, metrics, tracing, and structured error handling.
10. Uphold and evolve the architecture and engineering standards through technical design and code review.
11. Mentor engineers and raise the quality bar across the team.

Requirements

1. Proven full-stack delivery across a React/TypeScript frontend and a Python (or comparable) backend.
2. Sound judgment on architecture, API design, and data modeling, with the ability to articulate trade-offs.
3. Rigorous testing discipline and comfort with TDD and high coverage standards.
4. Ability to work with LLM/GenAI systems; direct experience preferred, though strong engineers eager to learn are welcome.
5. Technical leadership: thoughtful code review, clear technical writing, and a focus on root-cause fixes.

Nice to Have

1. Production experience with LLM/agent systems (LangGraph, LangChain, or similar).
2. Media or video pipelines: encoding, timeline editors, or serverless rendering.
3. Async worker systems (Celery, RabbitMQ, Redis).
4. Payments/billing and metering systems under concurrency.
5. Hexagonal architecture, ports-and-adapters, or DDD experience.

Growth

The role offers substantial influence over architecture and a clear path toward staff-level technical leadership for engineers who raise the team's standards.

Full stack Developer (Ref.ID: VRPL-FSD-Chn-0326)

No. of Positions: 2 Nos. Senior 1 No. Junior 1 No.

API Development & Architecture:

Design and develop scalable RESTful APIs using Node.js & TypeScript

Build modular, reusable, and clean backend architecture

Implement authentication & authorization (JWT / OAuth / Role-based access)

Ensure API security best practices

Write well-structured documentation (Swagger / Postman)

Real-Time Communication (Sockets):

Develop real-time features using WebSockets / Socket.io

Implement:

Live tracking

Real-time notifications

Status updates

Chat systems

Optimize socket performance and reconnection handling

Ensure scalability under concurrent user load

Database & Data Layer (PostgreSQL):

Design scalable and optimized database schemas

Write complex SQL queries & joins

Implement indexing & query optimization

Handle transactions and concurrency control

Work with ORMs like Prisma / TypeORM (preferred)

Maintain database security and backup strategies

Ensure data security and backups

Performance & Scalability:

Optimize API response times

Implement caching strategies (Redis preferred)

Design rate limiting and throttling mechanisms

Ensure system reliability & fault tolerance

Handle background jobs & queues

Deployment & DevOps Collaboration:

Deploy applications on AWS / GCP

Work with Docker containers

Implement CI/CD pipelines

Monitor logs & performance metrics

WhatsApp Business Integration:

Integrate WhatsApp Business API (Cloud / On-Premise)

Work with providers like Exotel / Twilio (preferred)

Implement Template messaging

OTP authentication

Notifications & alerts

Two-way communication

Manage webhook events and delivery tracking

Ensure compliance with WhatsApp policies

Required Skills:

Strong proficiency in Node.js

Hands-on experience with TypeScript

Strong experience in PostgreSQL

Experience building RESTful APIs

Experience with WebSockets / Socket.io

Good understanding of system design & architecture

Git version control

Knowledge of API security best practices

Preferred Skills:

Experience in real-time platforms (ride-hailing, delivery, chat systems)

Redis for caching

Message queues (Kafka / RabbitMQ / BullMQ)

Microservices architecture

Payment gateway integration

Cloud infrastructure knowledge

Soft Skills:

Strong analytical, problem-solving skills, and debugging skills

Startup mindset & ownership attitude

Ability to work in fast-paced startup environment and work independently

Good communication skills in all aspects

Procedure is hiring for WorkHero.

WorkHero is building the AI-powered back office for the skilled trades, starting with the $50B+ HVAC industry. Small contractors are great at their trade but lose 20+ hours a week to invoicing, permits, scheduling, and paperwork. WorkHero combines expert office managers with automation and AI tooling, enabling a small team to take real ownership of that back-office work

We’re hiring a senior engineer to own our real-time voice stack end to end—AI agents operating on live phone calls—and the data platform that turns those calls into insight: call → transcript → events → warehouse → dashboards. You’ll own meaningful systems end to end alongside a small, senior team with deep experience in AI, product, and the trades.

What you’ll build

• New product screens and flows (jobs, customers, invoices, scheduling) in React and React Native, especially AI chat UI (chat & tool result rendering, streaming responses, human review and feedback loops)
• AI workflows in production: tool-using agents, RAG/search, classification/extraction, and human-in-the-loop flows
• Automations: Contribute new features and improvements to our AI-powered business automation platform

In addition, you’ll own our first investments into a realtime voice stack and the call-data platform behind it. For example:

• Realtime voice agents on live phone calls: telephony/WebRTC integration, streaming speech-to-text and text-to-speech, turn-taking, interruption handling, and latency optimization
• Voice pipeline reliability: backpressure, failover, graceful degradation, and monitoring for live calls
• Call-data pipeline: transcripts, events, and structured extraction flowing from every call into the warehouse
• Analytics & dashboards: data modeling and conversation-intelligence features on top of call data
• Evals & monitoring for voice agents: quality metrics, drift detection, and cost/latency tracking
• Cloud infrastructure: scaling our platform with infrastructure as code, queues and orchestration, and CI/CD

Responsibilities

• Analyze requirements and propose innovative AI-native solutions to technical problems
• Write clean scalable code
• Own the voice and data stack end-to-end: design, build, test, deploy, and operate
• Optimize the performance, latency, and cost of our real-time AI systems
• Respond to critical system issues and ensure continuous system reliability
• Mentor team members and collaborate across teams, especially with product and subject matter experts
• Work to understand the needs of our users and think creatively about how to solve design challenges in your work
• This is a Remote role. We expect a minimum 4 hours overlap with the WorkHero team (11 AM - 3 PM ET).

Qualifications

• Senior-level backend experience (typically 5+ years) shipping production systems that you've owned
• Hands-on experience with realtime voice or streaming systems: telephony (SIP/Twilio), WebRTC, streaming STT/TTS, or frameworks like LiveKit or Pipecat — or comparable experience with demanding realtime/streaming infrastructure
• Data engineering fundamentals: event pipelines, data modeling, warehousing, and analytics on production data
• Strong proficiency in a typed backend language (TypeScript preferred; comparable experience welcome)
• Hands-on experience with LLM-powered features (usage, prompting, optimization, etc) and AI architectures
• The ability to work with infrastructure as code (terraform), cloud, and CI/CD systems at scale. We're a small team, so we own the whole stack!
• Excitement to leverage AI coding tools to their maximum benefit. We love Claude Code and Cursor and are constantly looking for better ways to leverage our time to build fast and build for scale.

Nice to have

• experience with voice-AI platforms (Vapi, Retell, Bland, Deepgram, LiveKit) or conversation-intelligence products (e.g. Gong-style analytics)
• experience scaling cloud infrastructure, especially AWS, and how to get the most out of key AWS services
• experience with workflow automation tools like n8n or Lindy
• experience with React for building internal dashboards
• experience with HVAC or back-office business workflows

WorkHero is committed to building a diverse team. We encourage candidates from all backgrounds to apply.

Role- Sr Senior Engineer 

Location -Hyderabad 

Shift -Night Shift starts from 8:30 PM IST 

About The Role

As a Senior Engineer on the team, you will own systems end to end across a high-volume, event-driven surface. You'll build the APIs and services behind key initiatives including:

- The driver comms platform — the rules engine, cohorting, and scheduling that reach drivers across SMS, push, and email

 - The onboarding funnel and applicant tracking layer, architected to support Veho's move to an in-house system over time

 - Live selling workflows that turn live offer and market-clearing data into well-timed, well-targeted driver outreach

You'll partner closely with Product, Design, Operations, and Data Science to ship tooling that scales with Veho's growth.

Responsibilities Include:

- Design, build, test, and deploy features across the driver-facing apps (driver mobile app, onboarding and registration surfaces), our GraphQL APIs, and the event-driven services behind them

 - Own a problem end to end: write the design doc your team works from, build it, roll it out behind a feature flag, and stay on it after launch until it is stable

 - Own the driver comms platform — the rules engine, cohorting, and scheduling that decide when to reach a driver over SMS, push, or email, and the delivery services behind it

 - Build the onboarding funnel and applicant tracking layer: registration and set-password flows, consent and eligibility gating, and the integrations that track where every applicant sits in the funnel

 - Build live selling workflows that consume live offer data and market-clearing signals to notify the right driver cohorts only when routes are actually claimable

 - Contribute to architectural decisions and technical design for complex, distributed systems, and architect today's comms and onboarding infrastructure so it supports the eventual in-house replacement of our third-party ATS rather than becoming throwaway work

 - Break a project into milestones your product, design, ops, and data partners can plan against

 - Write correctness-first code in an event-driven system where delivery is at-least-once and no driver may be texted twice about the same route — and where a send must never fire when there is no offer to claim

 - Improve reliability and observability in the services you own, with alerts that fire on real problems and stay quiet otherwise, so an on-call engineer is paged for a broken send pipeline and not for noise

 - Propose the engineering work your team should be doing, including the reliability, data-quality, and compliance work nobody is asking for

 - Use AI-native development workflows and tooling (Claude Code, Cursor, Copilot, and similar) as part of how you ship

 - Mentor the engineers around you, keep the team's code review bar high, and write down what you learn

What You Bring:

- 5–7+ years of experience building, testing, and deploying applications in high-traffic production environments

 - Depth in AWS serverless, including Lambda, a managed GraphQL layer such as AppSync, DynamoDB, EventBridge, and SQS

 - Experience with event-driven systems and the idempotency work that comes with at-least-once delivery — especially in a comms context where duplicate or misfired messages reach real people

 - Strong full-stack experience with TypeScript, Node.js, GraphQL, and React

 - Experience with DynamoDB single-table design

 - Experience integrating with third-party platforms and their webhooks, and keeping the data they produce in sync and trustworthy across services

 - At least one project owned through production rollout, including what broke afterward and who fixed it

 - Experience operating a service in production long enough to own its failure modes

 - Judgment about what a design costs in engineering time, dollars, and vendor commitment

 - A self-starter mindset with the ability to move quickly and ship iteratively, including on migrations, compliance obligations, and the manual steps nobody has automated yet

 - Enthusiasm for working closely with product, design, operations, data science, and support partners

-Tech Stack: AWS, TypeScript, Node.js, React, React Native, GraphQL, DynamoDB, serverless event-driven architecture; 

multi-channel comms (SMS, push, email); Firebase Auth; Statsig for experimentation; Redshift/Databricks-backed data pipelines

Preferred:

 - Experience with applicant tracking / recruiting funnels or other funnel-driven onboarding systems, and the drop-off analysis that improves them

 - Experience with experimentation and analytics infrastructure (Statsig, Fivetran, Redshift, Databricks) to measure and tune what you ship

 - Consent modeling and compliance obligations (e.g., driver terms-and-conditions and messaging consent) that span services