Golang Backend Engineer

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

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 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

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a Senior AI/ML Backend Engineer to help build the core intelligence layer powering Lumen and Agent Studio. You will design and ship production-grade backend systems that integrate LLMs into real agentic workflows — taking actions, retrieving knowledge and generating insights inside a live CRM product used by real businesses. This is a hands-on, build-focused role with direct ownership of systems that ship to production.

What You’ll Do

• Design, build and scale backend services in Python that power LLM-driven and agentic features within Lumen and Agent Studio.
• Build and productionize agentic AI systems — including planning, tool use, orchestration, memory and multi-step task execution.
• Integrate LLMs into core product workflows, focusing on reliability, latency, cost and correctness at production scale.
• Build robust APIs and services that connect AI agents with CRM data, business logic and third-party systems.
• Own evaluation, testing and monitoring for AI features to ensure they behave reliably in real-world, not just demo, conditions.
• Collaborate closely with product, design and other engineers to take features from zero to one and iterate rapidly based on real usage and customer feedback.
• Work directly with customers and customer-facing teams to understand real workflows, debug issues and translate feedback into product and engineering decisions.

What We’re Looking For

• 2–4 years of professional backend engineering experience, with strong hands-on Python skills.
• Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
• Should be hands-on with traditional Machine learning frameworks like Pytorch, Scikit-learn
• Solid understanding of API design, backend architecture, databases and distributed systems fundamentals.
• Familiarity with LLM orchestration concepts — prompting, tool/function calling, RAG, agent frameworks, evaluation and guardrails.
• Comfort working in a fast-paced, ambiguous, zero-to-one environment where you’ll be defining as much as building.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience with enterprise security, reliability or observability practices for AI systems.
• Prior experience working on CRM, SaaS or other enterprise business software.
• Exposure to voice AI or real-time systems.

About PortOne

PortOne is building the reconciliation and data intelligence layer for payments across Korea and international markets. We are a Series B startup backed by Softbank and Hanwa Capital, powering multi-billion dollars in annualised settlement volume for 2,000+ merchants across Korea, Thailand, Singapore, Indonesia, and beyond.

We are building AI-native products for leading brands — intelligent automation layers on top of complex financial data pipelines. If you want to work at the intersection of fintech, data engineering, and applied AI, this is your role.

Culture and Values

* You will be joining a team that stands for making a difference.

* You will be joining a culture that identifies more with Sports Teams rather than a 9 to 5 workplace.

* Your will have peers who are/have

** Highly Self Driven with A sense of purpose

** High Energy Levels - Building stuff is your sport

** Ownership - Solve customer problems end to end - Customer is your Boss

** Hunger to learn - Highly motivated to keep developing new tech skill sets

Your Work Ethic

* You are an athlete and building apps is your sport.

* Your passion drives you to learn and build stuff and not because your manager tells you to.

* You obsess over correctness — a bug in a settlement figure or a silent data drop is not acceptable to you.

* You have an eye for detail that most engineers skip past, and you take pride in getting it exactly right.

* Your work ethic is that of an athlete preparing for your next marathon. Your sport drives you and you like being in the zone.

* You are NOT a clockwatcher renting out your time, and NOT have an attitude of "I will do only what is asked for"

What will you do?

• Build and maintain financial data ingestion pipelines that pull settlement and transaction data from marketplace platforms (Amazon, Shopee, TikTok, Qoo10, Rakuten) on behalf of large brands operating across multiple Asian markets.
• Own reconciliation workflows end-to-end — from raw marketplace data to verified, merchant-ready settlement reports — ensuring every figure is correct and every discrepancy is surfaced, not swallowed.
• Design and implement AI-native features that automate financial analysis: agentic triage of settlement mismatches, root-cause detection across large transaction volumes, and intelligent alerting for ops and merchant teams.
• Instrument data quality and health monitoring so that silent failures — missing records, schema shifts, delayed ingestion — are caught before they reach the merchant.
• Build APIs and tooling that enable PortOne's ops and merchant success teams to investigate, verify, and close financial discrepancies faster and with more confidence.
• Expand platform coverage by integrating new marketplaces and new report types, working closely with data formats that are often inconsistent, undocumented, or changing without notice.
• Uphold rigorous engineering standards — correctness in financial data is not negotiable, and you treat edge cases and off-by-one errors with the same seriousness as a production incident.
• Uphold high engineering standards across codebases and processes.
• Collaborate with product, design, infrastructure, and operations stakeholders.

Skills and Experience

* Have ideally 2 to 4 Years of experience shipping high quality products/live features and workflows

* Strong backend engineering foundation — Go (Preferred), Python, or equivalent; REST/gRPC APIs; database design.

* Understands how to build scalable, resilient, and observable distributed systems.

* Must have built data flows and applications end to end taking full ownership

Preferred Skills and Background

*Prior experience/built apps in golang backend

*Data and data engineering background — comfortable with data pipelines, ETL/ELT patterns, event-driven architectures, reconciliation logic, or analytical workloads.

*AI-native development — you build products where AI is a first-class component, not a bolt-on.

*AI agentic development — experience building or working with agent frameworks, tool-use patterns, LLM orchestration, or automated reasoning pipelines.

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

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 Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

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

Location: Hyderabad, India. Based at the KnackLabs headquarters, with occasional travel to client locations for workshops and reviews. This role does not involve extended onsite deployments.

About the Role

You will work as an AI Architect who designs the systems behind our client engagements: AI agents, RAG systems, automation platforms, and the conventional backend systems around them.

This is a hands-on design role, not a slideware role. You will scope architectures with clients, make the hard technical decisions, defend them in review, and stay accountable for how the systems perform in production.

You will work directly with clients. Everyone at KnackLabs does. You will sit in design discussions with client engineering teams, present architecture decisions to technical and business stakeholders, and answer for the choices you make.

A full KnackLabs engineering team in Hyderabad builds with you. You own the technical design and the quality of what ships.

What you'll own

1. Architecture - Design AI agents, RAG systems, integrations, and the scalable backend systems around them, for multiple client engagements.
2. Technical scoping - Work directly with clients to turn a business problem into a system design, with clear trade-offs and clear reasons.
3. Scale and reliability - Make sure what we build handles real load: data stores, queues, caching, horizontal scaling, and fault tolerance.
4. Design reviews - Review designs and builds across engagements. Set the technical bar and hold it.
5. Evaluation strategy - Define how we measure accuracy, safety, latency, and cost for the AI systems we ship.
6. Guiding engineers - Raise the level of the engineers building with you, through reviews and direct pairing.
7. Feedback to the platform - Feed what you learn across engagements back into our platform and internal tools.

What we are looking for

1. Around 7 or more years of software engineering experience, including direct work with customers on design or delivery.
2. Full-stack development experience with strength in backend technologies.
3. Experience designing and building scalable applications. You understand how large-scale distributed systems work: data partitioning, queues, caching, horizontal scaling, and fault tolerance.
4. At least 2 years of strong, hands-on AI experience with large language models in production.
5. You build with AI coding tools like Claude Code or Codex as your default way of working. You understand Claude Skills, have written skills yourself, use them actively, and have contributed to them.
6. Hands-on experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
7. Hands-on experience building AI agents.
8. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
9. Experience with at least one cloud platform (AWS, Azure, or GCP).
10. Clear communication. You can explain an architecture decision to an engineer and to a business leader, and defend it under questioning.
11. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a design.

Nice to have

1. Experience building evaluations to measure accuracy, safety, latency, and cost.
2. Experience with observability and tracing tools such as LangSmith or Braintrust.
3. Experience with on-premises or private cloud (VPC) deployments.
4. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
5. Experience with data engineering and pipelines.
6. A history of side projects, open source contributions, or products you shipped end-to-end.
7. Experience working at a consulting or professional services firm in a client-facing delivery role.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open-source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, skills, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

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.

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.

We’re seeking a highly skilled, execution-focused Senior Backend Engineer with a minimum of 5 years of experience to join our team. This role demands hands-on expertise in building and scaling distributed systems, strong proficiency in Java, and deep knowledge of cloud-native infrastructure. You will be expected to design robust backend services, optimize performance across storage and caching layers, and enable seamless integrations using modern messaging and CI/CD pipelines.

You’ll be working in a high-scale, high-impact environment where reliability, speed, and efficiency are paramount. If you enjoy solving complex engineering challenges and have a passion for distributed systems, this is the right role for you.

Responsibilities—

• Design, develop, and maintain distributed backend systems at scale.
• Write high-performance, production-grade code in Java.
• Architect and optimize storage systems, ensuring efficient query performance and scalable data models.
• Implement caching strategies to reduce latency and improve system throughput.
• Build and manage services leveraging AWS cloud infrastructure.
• Develop resilient messaging pipelines using Kafka (or equivalent) for real-time data processing.
• Define and streamline CI/CD pipelines, ensuring rapid and reliable deployment cycles.
• Collaborate with product managers, frontend engineers, and DevOps to deliver end-to-end solutions.
• Monitor system performance, identify bottlenecks, and apply proactive fixes.
• Drive best practices in software engineering, testing, and code reviews. 

Requirements—

• 5+ years of experience in backend engineering, with deep hands-on coding experience.
• Strong proficiency in Java and familiarity with modern frameworks.
• Proven track record in building scalable distributed systems.
• Hands-on expertise with AWS services (e.g., EC2, S3, Lambda, DynamoDB, RDS).
• Solid understanding of messaging systems like Kafka, RabbitMQ, or similar.
• Strong grasp of query performance optimization and storage system design.
• Experience with caching solutions (Redis, Memcached, etc.).
• Familiarity with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.).
• Excellent problem-solving skills and ability to thrive in fast-paced environments.
• Strong communication and collaboration skills, with a proactive mindset.

Benefits—

• Best in class salary: We hire only the best, and we pay accordingly.
• Proximity Talks: Meet other designers, engineers, and product geeks — and learn from experts in the field.
• Keep on learning with a world-class team: Work with the best in the field, challenge yourself constantly, and learn something new every day.

About us—

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.

Passionate about education? Join us at CK-12 !!

CK-12 (www.ck12.org) is on the lookout for talented, creative, and dedicated people to join our mission to provide great education to students around the world. We are looking for candidates to join our office in Bangalore.

We have a strong education platform that has served over 352+ Million users, have got over 2.88+ Billion questions answered, and have more than 350 thousand customized Flexbooks. We have embarked on an exciting journey to build an AI-powered student tutor and Teacher Assistant to build the next generation of learning platforms.

About CK-12 Foundation

CK-12’s mission is to provide free access to open-source content and technology tools that empower students as well as teachers to enhance and experiment with different learning styles, resources, levels of competence, and circumstances.

To achieve this noble and ambitious vision, we at CK-12 are challenging the traditional model of education to transform it dramatically. Technology has opened up lots of opportunities to revolutionize education for the benefit of students, teachers and parents.

We have chosen to be non-profit so that we can effectively realize our mission and do the right thing! It also provides us with the ability to experiment with big and bold ideas. CK-12 is backed by Vinod Khosla, a renowned technology venture capitalist.

At CK-12, you’ll experience the benefits of working in a dynamic, entrepreneurial, innovative and non-bureaucratic environment where you will get a lot of cool things done than you ever imagined! We are a small group of passionate folks who are determined to disrupt the current form of education.

Technology is key to scale education and we deeply believe in it. Come develop great solutions on our cloud-based (AWS) and AI-first platform delivering rich and interactive content.

Does our mission, people and technologies excite you? If the answer is YES! and you are a great technologist who will challenge status-quo (no order takers please!) by innovating, please come join us! Together, we will change the world!

Check out our latest product offerings

• Introducing Flexi 2.0
• Flexi, our AI-powered Student Tutor – https://www.flexi.org/
• AI-powered Teacher Assistant – https://www.ck12.org/pages/teacher-assistant/

Location: https://goo.gl/maps/NkA2Hr8JhtE3raWr5

Backend Engineer

Basic

● Design, maintain, and monitor infrastructure for data products

● Design and develop RESTful APIs for the data infrastructure

● Design, implement and drive adoption of new analytic technologies and solutions

● Work closely with data scientists, front end engineers and peers to gather requirements and

develop solutions

● Handle and resolve issues escalated from the production operational environment.

● Troubleshoot performance, reliability, and scalability issues.

● Excellent Problem solving skills

● A Code Craftsman that follows best software development and coding practices delivering

understandable and maintainable code with thorough unit tests coverage

Requirements

● 10+ years of development experience with Java, Scala, and/or Python

● Experience with writing and executing queries on RDBMS and NoSQL databases. Working knowledge of MongoDB, MySQL. We are not looking for a DBA.

● Experience with ElasticSearch, OpenSearch, Vector Embeddings, and large-scale search systems.

● Experience designing and operating high-throughput, low-latency backend services in production environments.

● Experience with horizontal and vertical scaling strategies, load balancing, caching, rate

limiting, and performance optimization.

● Experience identifying and resolving application bottlenecks through profiling, monitoring, and performance tuning.

● Experience in virtualization technologies and deployment frameworks (familiarity with working on UNIX shell, Jenkins.)

● Bachelors or masters degree in computer science or equivalent

Desired

● Experience working with FastAPI/Pylons Web Framework.

● Familiarity with messaging and distributed processing systems such as RabbitMQ, Kafka, SQS, or Celery.

● Experience designing systems to support millions of requests, large datasets, and high-concurrency workloads.

● Adept at AI Coding assistants, leveraging them beyond prompting. Working experience in Skill and tool development and usage in AI Coding tools.

● Experience with AWS EC2, Redshift, RDS, S3