Agentic Tools Architect

Core Responsibilities

Operational Tools Architecture & Feature Planning 

• Own the feature roadmap for operational tools — Odoo, Redmine, GitHub, and adjacent systems — from business-process discovery through design, planning, and phased delivery 
• Serve as the subject-matter expert on how business processes (deal onboarding, delivery, support, finance, engineering operations) map onto tool capabilities, and where the gaps are 
• Design cross-tool workflows and integrations so that work items, code, and business records flow between systems as one coherent operational fabric 

Agentic Implementation & Modernization 

• Design agent-operable surfaces on each tool: typed, secure APIs, Model Context Protocol (MCP) servers, and webhook-driven automation that let AI agents read, create, and resolve operational work safely 
• Lead incremental modernization of legacy tool deployments — upgrading versions, retiring brittle customizations, and replacing manual processes with agentic workflows without disrupting live operations 
• Optimize agentic loops that operate on the tools for cost, speed, and integrity, with guardrails, audit trails, and human-in-the-loop checkpoints where they matter 

Plugin & Extension Engineering 

• Architect and build Redmine plugins in Ruby on Rails, Odoo modules, and GitHub Apps/Actions that extend the products cleanly rather than forking them 
• Engineer enhancements to be upgrade-safe across product versions — tracking upstream releases, isolating customizations behind stable interfaces, and planning migration paths 
• Prioritize contract-first API design between human intent, agent execution, and the underlying tools 

Governance & Quality Control 

• Act as the architectural reviewer for changes to Central tooling, including AI-generated pull requests and agent-authored configuration 
• Mandate testing and characterization coverage around customizations so upgrades and agentic changes land deterministically 
• Establish operational standards — access, data integrity, observability — for agents acting inside business-critical systems 

Required Qualifications 

• 10+ years in software engineering, business-systems architecture, or platform engineering, with deep SME knowledge of operational tools such as Odoo, Redmine, GitHub, or comparable ERP/ITSM/DevOps platforms 
• Expert-level proficiency in Ruby on Rails, including production experience building or maintaining Redmine plugins or comparable Rails-based extensions 
• Strong command of business processes across delivery, support, and operations — able to translate how a business runs into how a tool should behave 
• Practical agentic development experience: MCP, LLM tooling, AI-assisted automation, or agent-orchestrated workflows in production 
• Proven track record building plugins, modules, and extensions that survive product version upgrades, with disciplined API and integration design 
• Commitment to clean, tested, production-grade systems — including when the author of the code is an agent 

Preferred Qualifications 

• Python and Odoo module development experience; PostgreSQL fluency 
• Experience modernizing or migrating legacy tool deployments across major versions 
• Background in SaaS transformation, scale-up, or PE-backed environments 

Responsibilities

·        Build and operate the agentic loop: trigger → orchestration → agent execution → output to JIRA → human accept/reject → next agent, across design, coding, review, and testing agents.

·        Implement model routing and retry logic across a provider-agnostic model layer (e.g., Claude via AWS Bedrock, self-hosted or alternative models as cost/sovereignty hedges), including business-continuity fallback if a given provider becomes unavailable.

·        Own token cost control and context window management — per-agent and per-run budgets, circuit breakers that halt runaway execution, and cost observability tied back to JIRA.

·        Stand up and maintain observability, alerting, and monitoring across the agent fleet (e.g., Langfuse or equivalent), so agent health, cost, and quality are visible in real time.

·        Implement agent governance and safety guardrails: deterministic pre/post hooks gating every LLM call, kill switches, prompt injection prevention and mitigation, and audit logging.

·        Integrate the harness with JIRA as the system of record and other business systems as needed, ensuring every agent action, decision, and human override is tracked with no side channels.

·        Pair directly with client engineers throughout — this is capability transfer, not black-box delivery. You'll document, demo, and hand over as you build.

·        Work in outcome-based delivery stages (spike → architecture sign-off → build → pilot) with gated milestones tied to working software demos, not fixed artifact checklists.

·        Participate actively in team discussion and design decisions — this team expects engineers to challenge ideas constructively and speak up, not defer silently.

Must-Have Experience

·        Hands-on production experience building agentic systems(not tutorial-level or personal-project experience.) Candidates should be able to speak concretely about systems they've shipped.

·        Practical experience with agentic frameworks such as LangChain, LangGraph, or equivalent orchestration frameworks.

·        Experience with LLM orchestration and model routing across multiple providers/models, including fallback and retry design.

·        Working knowledge of agent governance: guardrails, human-in-the-loop approval flows, kill switches, and audit trails.

·        Practical understanding of prompt injection risks and mitigation techniques.

·        Experience with token cost management and context window/memory handling at production scale — this is a named governance requirement for the engagement, not a nice-to-have.

·        Strong Python (or equivalent) engineering background, comfortable working in AWS environments (Bedrock/AgentCore exposure a strong plus).

·        Experience with observability/monitoring tooling for distributed or agentic systems (e.g., Langfuse, Datadog, or equivalent).

·        Comfortable working with JIRA/Atlassian APIs or similar ticketing-system-of-record integrations.

Nice to Have

·        Direct experience with AWS Bedrock AgentCore, Temporal (or similar workflow orchestration), or LiteLLM-style model gateways.

·        Exposure to Cursor or other AI-native IDEs in a production engineering context.

·        Experience with self-hosted open-weight models (e.g., DeepSeek, GLM) as cost or sovereignty hedges alongside commercial APIs.

·        Financial services or other regulated-industry background.

·        Familiarity with Claude Code, Claude Cowork, or Claude Skills.

Qualifications

·        Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

·        3-5+ years in software/platform engineering, with a meaningful portion of that time specifically on agentic or LLM-orchestration systems (not general ML or data engineering alone).

Relevant Experience

·        Already built this kind of system and can talk through the trade-offs from experience, not theory.

·        Comfortable operating with ambiguity - technology choices (frameworks, specific models, tooling) are expected to evolve during the engagementand milestones are tied to outcomes rather than fixed deliverables.

·        Will contribute opinions - quiet execution without a point of view is not a fit for this team.

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. 

Job Description

This is a remote position.

Experience Level  

The Staff Software Craftsperson is a technical leadership role equivalent to an SDE-4/SDE-5. We are looking for seasoned engineers with 8+ years of hands-on experience who have transitioned from building great software to shaping the engineering culture and technical strategy of an entire organization. 

As a Staff-level crafter, your focus shifts from solving well-defined problems to identifying and solving the most complex, ambiguous technical challenges that impact our clients and our internal engineering standards. 

What You’ll Do as a Software Craftsperson  

• Technical Vision & Architecture: Lead the architectural direction for complex, large-scale systems. You will balance the immediate needs of the business with long-term system health, ensuring our solutions remain simple, scalable, and resilient. 
• Engineering Excellence at Scale: Champion disciplined engineering practices (TDD, Pair Programming, CI/CD) across multiple teams. You don’t just follow the bar; you raise it by introducing better patterns and tooling. 
• AI Strategy & Mentorship: Drive our AI-native development model. You will experiment with and define how we use AI to accelerate the SDLC, ensuring the team uses these tools to enhance—rather than replace—deep technical judgment. 
• Cross-Team Leadership: Act as a force multiplier. You will mentor senior engineers, conduct high-impact code reviews, and foster a culture of continuous learning and craftsmanship. 
• Strategic Client Partnership: Work as a technical consultant for high-stakes client engagements, translating vague business goals into robust technical roadmaps and navigating complex stakeholder environments. 
• Systemic Problem Solving: Beyond resolving production issues, you identify "class-level" problems in our infrastructure or codebase and implement systemic changes to eliminate them permanently. 

Requirements

What We're looking for 

• 8+ years of experience building and maintaining high-scale production systems. 
• Mastery of Fundamentals: Expert-level understanding of object-oriented design, functional programming, and system architecture (Microservices, Event-driven, etc.). 
• Polyglot Mindset: High proficiency in Ruby on Rails and React/Angular, with the proven ability to architect solutions across multiple tech stacks (Python, Go, etc.) as needed. 
• Influence without Authority: Demonstrated ability to lead through persuasion and technical excellence rather than just hierarchy. 
• Advanced AI Collaboration: Deep experience integrating AI into a professional workflow to explore architecture, automate toil, and peer-review logic. 
• Proven Ownership: A track record of taking a project from a nebulous idea to a successful, maintained production system. 
• Communication: Exceptional ability to explain complex technical trade-offs to non-technical stakeholders and executive leadership. 
• 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  

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.

Mus have expereince in Odoo , Redmine aand ROR

• 10+ years in software engineering, business-systems architecture, or platform engineering, with deep SME knowledge of operational tools such as Odoo, Redmine, GitHub, or comparable ERP/ITSM/DevOps platforms 
• Expert-level proficiency in Ruby on Rails, including production experience building or maintaining Redmine plugins or comparable Rails-based extensions 
• Strong command of business processes across delivery, support, and operations — able to translate how a business runs into how a tool should behave 
• Practical agentic development experience: MCP, LLM tooling, AI-assisted automation, or agent-orchestrated workflows in production 
• Proven track record building plugins, modules, and extensions that survive product version upgrades, with disciplined API and integration design 
• Commitment to clean, tested, production-grade systems — including when the author of the code is an agent 

About the Role

We are seeking a hands-on Tech Lead to design, build, and integrate AI-driven systems that automate and enhance real-world business workflows. This is a high-impact role for someone who enjoys full-stack ownership — from backend AI architecture to frontend user experiences — and can align engineering decisions with measurable product outcomes.

You will begin as a strong individual contributor, independently architecting and deploying AI-powered solutions. As the product portfolio scales, you will lead a distributed team across India and Australia, acting as a System Integrator to align engineering, data, and AI contributions into cohesive production systems.

Example Project

Design and deploy a multi-agent AI system to automate critical stages of a company’s sales cycle, including:

• Generating client proposals using historical SharePoint data and CRM insights
• Summarizing meeting transcripts
• Drafting follow-up communications
• Feeding structured insights into dashboards and workflow tools

The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.

Key Responsibilities

• Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
• Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
• Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
• Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
• Drive architecture decisions balancing scalability, performance, and security
• Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
• Mentor junior engineers and evolve into a broader leadership role as the team grows

Ideal Candidate Profile

Experience Requirements

• 5+ years in full-stack development (Python backend + React/JavaScript frontend)
• Strong experience in API and microservice integration
• 2+ years leading technical teams and coordinating distributed engineering efforts
• 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
• Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions

Technical Expertise

• Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
• Ensuring backend and AI systems are scalable, reliable, observable, and secure
• Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
• Experience building production-grade AI systems within enterprise SaaS ecosystems

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

Write clean, tested, production-grade code and participate actively in code and design reviews. 

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

Required Qualifications 

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs. 

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel). 

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

Job Title: Chief Agentic Systems Architect

Location: Remote

Type: Contract

ROLE OVERVIEW

We are hiring a Chief Agentic Systems Architect to transform a 10+ year legacy codebase into a high‑velocity, agent‑operable architecture. This role sits at the intersection of software architecture, AI‑agent orchestration, and engineering governance. You will design system patterns, MCP interfaces, and cognitive context layers that allow LLMs and autonomous agents to safely refactor, test, and ship production code with minimal human intervention.

WHAT YOU’LL DO

1. AGENT‑OPERABLE SYSTEM ARCHITECTURE

• Decompose legacy monoliths into agent‑readable, modular systems with strict boundaries and single responsibility
• Lead incremental modernization using the Strangler Pattern, wrapping legacy logic in modern, contract‑driven interfaces
• Enforce SOLID principles, Dependency Injection, and Hexagonal Architecture to ensure deterministic AI execution and low regression risk

2. AGENTIC FRAMEWORK & MCP LEADERSHIP

• Architect and maintain the agent context layer: standardized Skills, Rules, and Commands for AI‑driven engineering workflows
• Build and operate Model Context Protocol (MCP) servers exposing legacy APIs, services, and databases as typed, secure, AI‑consumable tools
• Own contract‑first API design as the primary interface between human intent and autonomous agent execution

3. ENGINEERING GOVERNANCE & AI QUALITY CONTROL

• Act as architectural gatekeeper for AI‑generated pull requests, ensuring scalability, security, and long‑term maintainability
• Mandate test‑driven development (TDD) and characterization testing to preserve legacy behavior during refactoring
• Monitor and optimize agentic reasoning loops to balance cost, speed, and architectural integrity

WHAT WE’RE LOOKING FOR

• 6+ years in software architecture, platform engineering, or technical leadership
• Proven experience modernizing large, undocumented legacy systems
• Deep hands‑on expertise with TypeScript, .NET, and Node.js
• Strong background in API design, distributed systems, and modular architectures
• Practical experience with agentic development, MCP, LLM tooling, or AI‑assisted engineering
• Bias toward clean code, deterministic systems, and production‑grade AI

NICE TO HAVE

• Experience with remote‑first or globally distributed teams
• Background in SaaS transformations, scale‑ups, or private equity-backed environments
• Comfort operating in high‑ambiguity, high‑ownership settings

About Us:

CLOUDSUFI, a Google Cloud Premier Partner, is a global leading provider of data-driven digital transformation across cloud-based enterprises. With a global presence and focus on Software & Platforms, Life sciences and Healthcare, Retail, CPG, financial services and supply chain, CLOUDSUFI is positioned to meet customers where they are in their data monetization journey.

Our Values

We are a passionate and empathetic team that prioritizes human values. Our purpose is to elevate the quality of lives for our family, customers, partners and the community.

Equal Opportunity Statement

CLOUDSUFI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified candidates receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation and national origin status. We provide equal opportunities in employment, advancement, and all other areas of our workplace. Please explore more at https://www.cloudsufi.com/

Role :

A Software Engineer who builds the tools this company runs on. You build agent loops, and the loops build the solutions. You work towards a Company Brain that anyone here can ask.3–5 years’ experience · Reports to the CFO · 

THE KEY SKILL

You build the agent loops that build the solutions. You will not write every automation by hand. You build the loops that

write them. Ship a prototype every week. You ship something every day.

You’ll be building a Company Brain with access control. One system that holds what the company knows about finance,delivery and people. Anyone can ask it a question. Each person sees only what they are cleared to see.

One hard filter. If you cannot write and debug production code, and have not done it before, please do not apply.

CORE RESPONSIBILITIES

• Work the backlog: You pick items off a live, ranked backlog. You learn each function by building inside it. There is no discovery phase. What you learn goes back into the backlog and changes what comes next.

• Build the product: You design, build and ship tools that people use every day. Reconciliation, MIS, the deal desk,quote to cash, or whatever the real bottleneck turns out to be. You choose the tools and frameworks.

• Wire the data: Connect the systems each team already uses, so that the same number means the same thing everywhere.

• Make it visible: You build live dashboards and alerts that leaders read on their own, instead of asking someone for a report.

• Keep it running: You own uptime and accuracy for everything you build. Anything that touches money or people needs a person in the loop.

THE STACK

• Build with: Python and TypeScript. You write production code. Frontier model APIs from Anthropic, OpenAI or Google, with tool calling and structured output. At least one agent framework. MCP to connect agents to internal systems.Postgres and pgvector, or something similar, for retrieval. You deploy on GCP, and you debug your own work.

• Work in agents daily: Claude Code, Cursor or something like them, as the way you write code every day. You should have a clear view on how to run the loop, and on when a person has to step in.

• Connect to: The systems we already run on for accounting, CRM, hiring and IT support, along with Google Workspace.Most of the work is getting them to agree with each other.

• Check what you ship: Anything that produces a number needs a way to catch it going quietly wrong. Test sets, regression checks, and alerts on the output as well as on the job.

WHAT GOOD LOOKS LIKE

• Something you built is running by week two, and someone is using it.

• By day 90, time spent on reconciliation or reporting is down by a number you can defend to the CFO.

• Every tool you ship has a named owner who is still using it 60 days later. That is the measure that counts.

• Leaders stop asking for numbers, because they can already see them.

• By the end of your first year, a first version of the Company Brain answers real questions about Finance, and each

person who asks sees only what they are cleared to see.

WHO THIS IS FOR

• You have built products: 3 to 5 years at a software product company, on a product with real users at scale. That means 100k+ monthly active users, or heavy daily use by a large enterprise customer base. You have owned code in production, in front of real users, long after it shipped.

• You ship alone: You are comfortable as the only engineer in the room, and the only person on call for what you built.

• You work out new ground fast: A domain you do not know is interesting to you. You start without waiting for a spec or an expert.

• You are fluent with agents: One person cannot cover a whole company by hand. You use agent loops heavily and youare good at it.

• You write and speak clearly: Half this job is pulling a process out of a finance or delivery lead and giving it back to them correctly. You work remotely, so this matters a great deal.

HOW WE WILL ASSESS

• A design problem: Live. We give you a function of the B2B company, and you design the system for it. We watch how you break down a domain you do not know, how you size it, and what you leave out on purpose.

• A build exercise: Live and screen-shared, on your own setup, with your own agents. You build the way you normally build. We watch how you run the loop, when you step in, and what you decide to skip.

• Your work and your questions: We talk about what you have shipped before. You ask us whatever you want.

Communication is not a separate round. All three sessions are live, and how clearly you explain your thinking is part of how we judge you.

WHERE IT LEADS

You report to the CEO and CFO from your first day. Your charter covers the whole company. Nothing sits between you and production. Very few engineering jobs offer all three at once, and that is why this one exists.

In 18 months you will know how this company really runs: the data, the money, and the gaps between teams. The rolethen changes shape to fit whatever the biggest open problem is by then.

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.