SDE II - Software Engineer II - Backend/AI Agentic/AI Native

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 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.

2–4 years of experience in backend development with Python or Golang.

● Solid understanding of RESTful APIs, microservices, and distributed systems.

● Strong knowledge of data structures, algorithms, and OOPS principles.

● Hands-on experience with relational and/or NoSQL databases.

● Familiarity with Linux development, Docker, and basic cloud concepts

(AWS/GCP/Azure).

● Proficiency with Git and version control workflows.

● Familiarity with AI-powered development tools or exposure to projects involving large

language models (LLMs) is a plus.

Strong analytical and debugging skills with the ability to solve complex problems.

About the team

SecurITe’s mission is to build an Agentic‑AI driven security platform that protects critical infrastructure from modern cyber threats. Our focus is on delivering highly performant, resilient, and intelligent network security systems that help defenders stay ahead of adversaries.

About the Role

We’re looking for a Senior Golang Engineer to spearhead the development of our next-generation cybersecurity AI platform. In this role, you will build and scale robust backend systems to solve complex, high-impact technical challenges.

Why This Role Matters

Cybersecurity is undergoing a fundamental shift. AI is no longer an enhancement—it’s becoming the core engine of how detection, investigation, and response are executed. As our Golang Engineer, you will architect and build the backend systems that make this transformation real. Your work will influence not only the evolution of our platform, but also how an entire industry adapts to and leverages AI‑driven security. This is a chance to solve novel technical challenges while shaping how defenders outpace modern attackers.

What You’ll Do

●    Design and develop scalable backend services and REST APIs using Golang.

●    Architect and maintain microservices-based systems.

●    Perform rigorous debugging and system performance optimization.

●    Integrate services with Opensearch, Elasticsearch, and Sumo APIs.

●    Manage and optimize data interactions with SQL databases.

Impact You’ll Have

●    Build the foundational infrastructure that enables real-time AI-driven threat detection.

●    Ensure the reliability and performance of critical security systems under high-load scenarios.

●    Directly contribute to the resilience of global critical infrastructure by shipping secure and efficient code.

Required Experience

●    4-8+ years of experience in Golang and REST APIs

●    Extensive experience in professional backend software development.

●    Proven track record of designing and deploying distributed systems at scale.

●    Demonstrated experience optimizing performance for data-intensive applications.

●    Proven experience leading or architecting large-scale distributed systems and driving technical strategy.

Required Skills & Qualifications

●    Expertise in Golang development.

●    Deep experience building Backend/REST APIs.

●    Proficiency in Python.

●    Strong understanding of Microservice architecture.

●    Experience with SQL databases.

●    Experience with Opensearch, Elasticsearch, or Sumo APIs.

●    Strong capabilities in debugging and performance optimization.

Nice to Have

●    Knowledge of Identity, Authentication, and Authorization protocols (OAuth, SAML, JWT).

●    Experience working in Linux environments.

The Mindset

●    Problem Solver: You thrive on complex, ambiguous challenges and engineer elegant solutions.

●    Ownership‑Driven: You take initiative, move fast, and deliver outcomes without hand‑holding.

●    Continuous Learner: You stay ahead of the curve in AI, ML, and emerging technologies.

●    Startup DNA: You excel in fast‑moving environments where priorities evolve and impact is immediate.

Required Experience: 10–15 years (with at least 3–5 years in leadership roles)

● 10–15 years of overall experience in backend engineering, with strong exposure to

Python and/or Golang.

● 3–5 years of experience managing engineering teams.

● Proven experience delivering large-scale, distributed systems in production

environments.

● Strong understanding of microservices, cloud-native architecture, and DevOps

practices.

● Hands-on background in backend engineering (able to guide teams technically, even

if not coding daily).

● Familiarity with CI/CD pipelines, observability, and performance optimization.

● Experience in financial services or high-transaction domains is a plus.

● Experience leading teams that have utilized AI-driven development practices (e.g.,

agentic coding, LLM integration) to improve productivity and innovation is a

significant advantage.

Skills

● Excellent leadership and people management abilities.

● Strong communication and stakeholder management skills.

● Ability to balance technical depth with business priorities.

● Problem-solving mindset with a focus on delivery and impact.

● Passion for building engineering culture and improving developer experience.

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

We are looking for an Engineering Lead to own the entire technology stack — from onboarding and underwriting to disbursals, repayments, and collections — and to build the engineering function into something genuinely AI-native. 

What You'll Own 

● Full tech stack: backend, frontend, infrastructure, integrations, and data pipelines 

● Real-time underwriting and decisioning systems 

● LOS/LMS architecture — onboarding, disbursals, repayments, and collections

● Integrations with bureaus, KYC providers, account aggregators, and payment gateways 

● Reconciliation systems — disbursement, repayment, and NACH reconciliation end-to-end 

● AWS infrastructure: scaling, reliability, uptime, and cloud cost ownership ● Data infrastructure for the credit and risk team — feature pipelines, model serving, experiment infrastructure 

● Engineering leadership: hiring, sprint planning, code reviews, and execution standards 

● Compliance systems: RBI guidelines, DPDP, KYC/AML, e-NACH, e-sign 

AI-Native Engineering 

This is a core part of the role, not a bonus. You will build a machine-readable knowledge base of the entire codebase — architecture, data models, service contracts, coding standards, decision history — so that AI agents working on code have the context to produce accurate, consistent output. You will build skills for code review, developer onboarding, and recurring engineering workflows. You will build a code review pipeline where agents do the first pass on every pull request. The knowledge base and the skills improve over time as the team grows and the product evolves. 

What We're Looking For 

● 7+ years in software engineering, with at least 2 years leading teams or architecture

● Strong hands-on experience with Python, Django, and React Native

● Deep expertise in AWS and cloud-native architecture 

● Experience with both SQL and NoSQL databases 

● Strong understanding of distributed systems, microservices, and API design

● Experience owning reconciliation or payment flow infrastructure in a lending or payments context

● Prior experience in fintech / NBFC / digital lending — mandatory

● Strong understanding of the full loan lifecycle — mandatory 

● You have used LLMs seriously as engineering tools and have strong opinions about what makes AI-assisted development produce good output versus mediocre output 

Bonus: Kubernetes / Kafka, AI/ML-driven underwriting, Account Aggregator framework, e-NACH / e-Sign / Video KYC integrations 

What Success Looks Like 

● scales with strong uptime, performance, and reliability

● Reconciliation runs cleanly — no financial discrepancies surface late ● A new engineer joins and is writing standard, correct code within their first week

● The credit team is never blocked on an engineering dependency 

● Engineering health metrics are tracked and visibly improving 

● AI agents are doing the structured first pass on code reviews, and the system gets smarter over time 

About Us

We believe the future of software development is AI-native — where engineers operate at a higher level of abstraction and quality remains non-negotiable. 

Incubyte is a software craft consultancy where the “how” of building software matters as much as the “what”.  

We partner with companies of all sizes, from helping enterprises build, scale, and modernize to early-stage founders bring their ideas to life. 

Our engineers operate in an AI-native development model, using AI as a collaborator across the SDLC to accelerate development while upholding the discipline of software craftsmanship. Guided by Software Craftsmanship and Extreme Programming practices, we build reliable, maintainable, and scalable systems with speed, without compromising quality. If this way of building software resonates with you, we’d like to talk. 

Our Guiding Principles 

These principles define how we work at Incubyte. They are non-negotiable. 

Relentless Pursuit of Quality with Pragmatism 

  We build high-quality systems without losing sight of delivery. 

Extreme Ownership 

  We take responsibility end-to-end for decisions, execution, and outcomes. 

Proactive Collaboration 

  We collaborate closely, challenge each other, and solve problems together. 

Active Pursuit of Mastery 

  We continuously improve our craft and raise our bar. 

Invite, Give, and Act on Feedback 

We seek, give, and act on feedback to get better every day. 

Ensuring Client Success 

We act as trusted partners and focus on real outcomes, not just output. 

Experience Level

This role is ideal for engineers with total 3+ years of experience with a proven track record of shipping complex projects successfully.

An experienced individual contributor and leader who thrives in large, complex projects with widespread impact.

What You’ll Do as a Software Craftsperson 

• Design and build high-quality, maintainable systems using disciplined engineering practices such as TDD, continuous refactoring, and pair programming 
• Operate in an AI-native development model, using AI as a collaborator to explore architecture and design, accelerate development, and continuously improve systems while applying strong judgment to ensure that speed never compromises quality 
• Take end-to-end ownership of outcomes from problem understanding and system design to implementation, deployment, and operation in production 
• Make thoughtful design decisions that balance simplicity, scalability, and long-term maintainability in real-world systems 
• Maintain a high bar for engineering quality through rigorous testing, code reviews, and continuous feedback 
• Investigate and resolve production issues, and implement systemic improvements to prevent recurrence 
• Work directly with clients, navigate ambiguity, and translate business problems into well-designed technical solutions 
• Contribute to improving team practices, tooling, and systems to raise the overall quality and effectiveness of engineering 

Requirements

What You’ll Bring 

• 3+ years of experience building high-quality, production systems (flexible based on demonstrated capability) 
• Strong fundamentals in software engineering, including object-oriented design, system design, and testing practices such as TDD 
• Demonstrated ability to build simple, maintainable, and scalable systems with a focus on long-term reliability 
• Proficiency in one or more modern technologies, Python, PHP, JavaScript, or TypeScript, with the ability to learn new technologies quickly 
• Deep experience working with Git in collaborative environments, including managing shared codebases, conducting code reviews, and maintaining a high bar for quality 
• Ability to operate effectively in an AI-native workflow using AI as a collaborator to explore solutions and accelerate development, while applying strong judgment to ensure correctness, quality, and maintainability 
• Clear thinking and strong problem-solving ability, with the capacity to break down complex problems into simple, well-structured solutions 
• A strong sense of ownership — you take responsibility for outcomes, care deeply about quality, and are not comfortable shipping work that does not meet your standards.

Benefits

Life at Incubyte 

We are a remote-first company with structured flexibility. Teams commit to shared rhythms during core hours, ensuring smooth collaboration while maintaining autonomy. Twice a year, we come together in person for a co-working sprint and once a year for a retreat - with all travel expenses covered. 

Our environment is built for crafters: pairing, refactoring, experimenting with AI, and pushing the boundaries of software excellence. We are all lifelong learners, and our work is our passion. 

Perks

• Dedicated learning & development budget. 
• Sponsorship for conference talks. 
• Comprehensive medical & term insurance. 
• Employee-friendly leave policies. 
• Home Office fund 
• Medical Insurance

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.