Chief Agentic Systems Architect

Read This Before Anything Else

We have 6 developers who can ship. What we don't have is someone who turns that into a real engineering function: real architecture, real leverage, real AI-driven advantage. If that gap sounds like an opportunity rather than a headache, you're in the right place. If it sounds like a lot of undefined work with no playbook handed to you, this one probably isn't for you. That's completely okay. There are plenty of great roles that fit differently.

About CraftMyPlate

CraftMyPlate is Hyderabad's go-to platform for food experiences for micro-events: house parties, birthdays, office celebrations, festive gatherings, and more. We're building the operating system for how India discovers, customises, and orders food for smaller events. We're backed by established founders and investors, and we're funded and growing fast. The next phase of that growth runs through engineering.

Where We Stand

Some numbers, because they matter more than adjectives. Order volume has grown 50x in two years, and we're compounding at roughly 3x year over year, without giving up equity to fund it. That means the business runs on its own economics. The growth is real demand, not runway bought with dilution, and every efficient architectural decision this role makes directly protects that.

Most people size up an opportunity by asking what's going to change in ten years. The more useful question, and the one this company is built around, is what won't change. People will keep gathering. They'll keep celebrating, hosting, and marking festivals, in 10 years and in 20. That permanence is the bet. You're not building infrastructure for a trend cycle. You're building for a category that outlasts the current AI wave, the next funding round, and probably us too.

The Technical Reality

Here's an honest read of the engineering problem, not a sanitized version of it.

Event-driven commerce doesn't scale like typical e-commerce. Demand isn't smooth, it's spiky: weekends, festival calendars, and event dates create real load concentration, and each order is tied to a hard deadline that can't slip the way a shipped package can. That has direct architectural consequences: systems need to handle bursty, unpredictable traffic without paying for idle capacity the rest of the time, which is exactly why we're serverless-first on AWS rather than running a fixed fleet sized for peak.

Underneath that, every order touches multiple systems that have to stay consistent: kitchen and vendor fulfillment status, inventory across partners, payment gateway settlement, and refunds, often in real time and often across more than one vendor for a single event. Getting that consistency right across SQL and NoSQL stores, without it becoming a source of support tickets and manual reconciliation, is a real architecture problem, not a CRUD problem.

The AI-agent layer is the next lever, and it's a business lever as much as a technical one. Every workflow we can hand to a well-orchestrated agent instead of a new hire is a workflow that scales without adding headcount, which is exactly how a company grows 3x a year without diluting equity to fund the team behind it. That's why agent orchestration across multiple LLMs, using LangGraph, sits in the "go deep" tier of this role rather than being a nice-to-have.

You'll likely find some of this framing right and some of it worth challenging once you're actually in the codebase. That's expected, and honestly preferred over someone who just nods along.

Why This Role Exists

You'll be the most senior technical person in the company, reporting directly to the founder. Not a manager brought in to run standups. An owner. You set the architecture, you write code yourself, and you make the team materially better. You also own where AI and automation take this company next, starting with our first in-house AI agent product (details shared in the interview), and expanding from there into how the company runs, department by department: HR, finance, marketing, design, development, all sitting on an engineering layer that you design.

If you've outgrown a role where you plan but don't build, or where good ideas die in a committee, this is built to be the opposite of that.

What You'll Own

• Architecture, end to end. Scalable, cost-efficient systems from day one, not "fix it later" engineering. You own the decisions and their long-term consequences.
• Hands-on building. You are still writing code and shipping. This isn't a seat where you review other people's work all day. You lead by building.
• The engineering team. Directly manage, mentor, and level up our 6 developers. Build the technical bar, the review culture, and the calibration that lets the team ship independently.
• The AI-agent roadmap. Own the architecture behind our first AI agent product, then the broader strategy for AI agents and automation across every function in the company, with engineering as the layer underneath all of it.
• Team scaling. Build the next layer of leads under you so execution quality scales without you being the bottleneck.
• Technical accountability. When something breaks, you fix it. You don't escalate and wait.

Our Stack, and the Depth We Expect

Not everything on this list needs the same level of mastery. Some of it you need to own at an architectural level. The rest you need to be strong enough to build yourself, direct the team on, or delegate to AI agents with confidence.

Go deep here. This is where the real architecture decisions live, and where the business impact is highest:

• AWS, serverless first. You should be genuinely well versed in AWS application development, not just "have used AWS." You should be able to design and guide serverless architecture (Lambda, API Gateway, DynamoDB, Step Functions, and similar) as our default way of building, because our demand curve is spiky by nature and fixed infrastructure is money left on the table.
• TypeScript, our primary language across backend and frontend.
• Agent orchestration across multiple LLMs, using LangGraph. This is core to our AI roadmap and our path to scaling operations without scaling headcount. You own how it's architected, not just how it's used.

Working proficiency. Build it yourself, direct the team, or hand it to an AI agent and know if the output is right.

This Is You If

• You've built and shipped real production systems yourself, not just reviewed other people's architecture from a distance.
• You go deep wherever the problem is, and you're comfortable owning the exact stack described above, not just "full-stack" in the abstract.
• You've made engineers around you measurably better, whether or not you've held the title for it yet.
• You're already using AI coding tools and agents seriously, like Claude, Cursor, or similar tools, as part of how you build, not as something you tried once. We'll likely explore this together in the interview.
• You have a bias toward leverage over hours. You'd rather automate or systematize a problem than grind through it. But when something's live and needs to be done right, you see it through completely, with no half-finished work.
• You want to build something for years, not land somewhere comfortable. We'll know the difference from how you talk about your last three years.

This Might Not Be the Right Fit If

• You'd prefer a stable, well-defined role with clear boundaries and someone else making the calls. That's a fair thing to want, just not what this is.
• You'd rather receive direction than bring us architecture and AI strategy yourself.
• You haven't yet gotten hands-on with AI coding tools in your daily work.
• You're drawn more to the title than the work behind it.

If none of that sounds like you, we'd love to hear from you.

Requirements

• 5 to 7 years of experience in software engineering, with real ownership of architecture-level decisions, not just feature delivery.
• Prior experience leading or mentoring engineers, formally or informally.
• Tier-1 or Tier-1+ engineering college strongly preferred (IIT, BITS, top NIT tier, or equivalent). We'll consider other institutions only with clearly commendable, verifiable work: real systems you can walk us through in depth, strong open-source contributions, or a track record that speaks for itself. Pedigree is a proxy for speed, not a checkbox. We test for the underlying ability regardless.
• Comfortable in an early-stage environment: undefined problems, few processes, and the expectation that you help define both.

Compensation

Competitive, with equity. We're formalizing a structured ESOP program alongside this hire. Specific numbers are discussed directly in later interview rounds.

If reading this got you a little excited about what you'd build here, we'd genuinely love to talk. If it didn't quite land, no hard feelings. We just want the right fit for both sides.

Position Overview

We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and

maintain end-to-end software solutions spanning web platforms, desktop applications, and

autonomous AI agents capable of interacting with and controlling these software systems.

The ideal candidate will bridge traditional engineering software with cutting-edge artificial

intelligence to automate data processing and enhance operational decision-making. While

not strictly required, a background or strong interest in the energy sector—specifically

drilling and completion operations—is highly desirable.

Key Responsibilities

• Full Stack Development: Design, develop, and deploy robust web applications and

native desktop software utilized by engineering and operational teams.

• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-

driven workflows capable of interpreting data, executing commands, and safely

controlling desktop and web-based software.

• Workflow Automation: Translate complex workflows into intuitive software features

and autonomous agent actions, minimizing manual data entry and operational

bottlenecks.

• Data Integration: Handle high-frequency data streams and integrate them seamlessly

into user interfaces and backend AI models.

• Architecture & Scalability: Ensure high performance, security, and scalability across

cloud infrastructure (AWS/Azure), local desktop environments, and potential edge

computing setups.

• Cross-Functional Collaboration: Work closely with domain experts and end-users to

translate field challenges into technical product requirements.

Required Qualifications & Experience

• Experience: Minimum of 5 years of professional software development experience,

with a proven track record of delivering production-ready web and desktop

applications.

• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at

least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend

frameworks (React, Angular, or Vue.js), Node.js, and desktop application development

(Electron, WPF, Qt, or Tauri).

• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs

(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,

AutoGPT, or custom agent architectures.

• Automation & UI Control: Expertise in software control mechanisms using tools like

Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to

allow AI agents to navigate software.

• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud

platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.

Preferred Qualifications (Strong Plus)

• Industry Domain Expertise: Prior hands-on development experience within the oil and

gas sector, specifically focused on drilling, completions, rig operations, or subsurface

engineering software.

• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and

time-series databases.

• Experience deploying AI models and agents in edge or low-connectivity environments

(such as offshore rigs or remote drilling sites).

• Familiarity with safety-critical software design and cybersecurity standards in

industrial control systems (ICS/SCADA).

• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.

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.

Job title: Chief Agentic Quality Architect

Type: Full-Time | Contract

Location: Remote

Role Overview

Equity Partners builds profitable growth by acquiring and operating enterprise software companies — refining a proprietary operating model across 40+ acquisitions and two decades of hands-on experience, now supercharged by our patented agentic AI platform. We're hiring a Chief Agentic Quality Architect to lead the transition from traditional scripted testing to an AI-augmented quality ecosystem. In this role, you'll audit and rebuild our quality engineering foundations, deploy agentic automation across critical business flows, and build the guardrails needed to keep AI-generated code production-ready.

Key Skills

• 10+ years in QA automation engineering, SDET, or test architecture roles
• Expert-level proficiency in Playwright, Cypress, or Selenium
• Hands-on experience using LLMs (Claude, GPT-4, etc.) and agentic frameworks to generate code or automate workflows
• Deep understanding of integrating quality gates into AWS-based CI/CD pipelines or similar environments
• Architectural mindset, with the ability to design "Behavioral Snapshots" to safeguard critical business logic during rapid transformation

Responsibilities

• Conduct a comprehensive audit of the existing test estate across unit, integration, API, UI, sanity, and regression layers, producing a Current State & Gap Coverage Report
• Architect and own a phased quality engineering roadmap across two-week, one-month, and three-month delivery horizons
• Deploy agentic test generation — transforming business requirements into executable Playwright/Cypress scripts, generating synthetic test data, and implementing self-healing automation
• Design regression strategies and quality gates specifically tuned to catch hallucinations and logic errors in AI-generated code
• Build, mentor, and upskill a specialist QA team fluent in AI-assisted testing and agentic automation frameworks

If you're ready to rewrite the rules of quality for an AI-native world, we want you leading the charge.

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.

Build AI where the work actually happens.

Celeco works inside real businesses to understand critical workflows, ship production AI systems and stay through adoption.

We are hiring our first Intelligence Architect (Forward-Deployed AI Engineer).

⌁

Full-time · Remote-first · Optional hybrid in Bengaluru

At least one year of professional engineering experience. Customer travel when the work requires it.

The role

An Intelligence Architect enters a customer environment with an unfinished question and leaves behind a working, measurable system.

This is a hands-on engineering role at the boundary of product, operations and customer delivery. You will learn the domain, inspect the existing systems and data, decide what should be built, and write the code that puts it into production.

What you will own

• Interview and shadow the people doing the work, then map the real workflow, including hand-offs, exceptions and workarounds.
• Understand the customer's application stack, APIs, data, identity model, security constraints and deployment environment.
• Turn business goals into a technical scope, system design, delivery plan and measures of success.
• Build across the stack: AI workflows, data pipelines, integrations, backend services and the interfaces people use.
• Choose the simplest reliable approach. The answer may combine agents, retrieval, rules and conventional software.
• Create evals from representative cases, define quality and failure metrics, inspect traces, red-team the system and set launch thresholds.
• Make practical trade-offs across accuracy, latency, cost, privacy, security and speed.
• Take prototypes into production with tests, monitoring, access controls, documentation and a plan for failure and recovery.
• Work beside customer teams during rollout and improve the system until it becomes part of the workflow.
• Turn what works into reusable components, evaluation sets and playbooks for future Celeco deployments.

You will probably thrive here if

• You have at least one year of professional software or product engineering experience.
• You have shipped a real system used by other people and can explain what you owned, what broke and what you changed.
• You are strong in Python or TypeScript and comfortable moving across unfamiliar codebases, APIs, databases and cloud services.
• You have built with language or multimodal models and understand prompting, structured outputs, retrieval, tool use and model failure modes.
• You use Cursor, Codex, Claude Code or similar tools as part of your engineering workflow, while still reviewing, testing and understanding the code you ship.
• You can create an evaluation set, choose useful quality metrics and improve a system through error analysis instead of prompt guesswork.
• You can speak with an operator, an engineering team and a senior leader without losing the thread of the problem.
• You work well with incomplete requirements, write clearly and surface risks early.
• You care about whether people use what you build and whether it changes a business outcome.
• You can commit full-time and travel to customer sites when discovery or rollout is better done in person.

Experience with cloud deployment, containers, CI/CD, observability, enterprise integrations, authentication or security is useful. We do not expect one person to arrive knowing every framework, cloud or industry.

What you will get

• Direct ownership of live customer problems from discovery through production.
• Close collaboration with Celeco's founders and customer leadership teams.
• Exposure to different industries, operating models and technical environments.
• The freedom to choose the technical approach and the responsibility to prove that it works.
• A role in defining Celeco's engineering methods, reusable systems and technical culture while the company is early.
• A remote-first setup, with the option to work together in Bengaluru.

If the work sounds like you but your background is unconventional, apply. We care more about what you have built, how you think and how quickly you learn than pedigree.

Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment.

The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. 

This is not a staff-augmentation seat and not an advisory role. You ship.

What you will do?

• Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
• Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
• Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
• Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
• Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.

What are we looking for?

• Excellent communication skills (English) — verbal and written. Non-negotiable. You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. If you can build but not explain, this role is not a fit.
• You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke.
• Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists:
• Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies.
• Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent).
• Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each.
• Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do.
• Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition.
• RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data.
• Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
• Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
• Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
• Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work.

You'll be preferred if you've:

• US English verbal and written fluency 
• Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
• Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
• Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
• Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
• Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
• Prior customer-facing consulting or forward-deployed experience.
• AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).

Why This Role?

• You own outcomes, not tickets. FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours.
• You work agent-native from day one. Our delivery model would not function without agents. You build with the platform, not around it.
• You ship. Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots.
• You compound. Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.

We're Hiring: Agentic Tools Architect

Contract | Remote-first | Global

We're currently recruiting for one of our clients, A company that specializes in acquiring enterprise software businesses and transforming them into AI-native, profitable, scalable operations. They run remote-first, globally distributed teams, and their internal operations platform (Odoo, Redmine, GitHub) currently runs on human-shaped workflows. They're looking for someone to rebuild it for agents.

The Role

Redesign the client's operational tooling so AI agents can create, route, and resolve work alongside people — with minimal oversight. You'll own the roadmap, build the plugins (Rails-first), and set the guardrails.

You'll:

• Own feature roadmap across Odoo, Redmine, GitHub & adjacent systems
• Design agent-operable APIs, MCP servers, and automation
• Build Rails-based Redmine plugins, Odoo modules, GitHub Apps that survive version upgrades
• Review AI-generated PRs and agent-authored config; set standards for access, integrity, observability

You bring:

• 10+ years in software/platform engineering with deep SME knowledge of Odoo, Redmine, GitHub, or similar
• Expert Ruby on Rails, production Redmine plugin experience
• Real-world agentic dev experience (MCP, LLM tooling, agent workflows)
• Strong grip on delivery/support/ops business processes

Nice to have: Python + Odoo modules, PostgreSQL, legacy modernization experience, PE-backed/SaaS scale-up background

We are building an advanced, AI-driven multi-agent software system designed to revolutionize task automation and code generation. This is a futuristic AI platform capable of:

✅ Real-time self-coding based on tasks  

✅ Autonomous multi-agent collaboration  

✅ AI-powered decision-making  

✅ Cross-platform compatibility (Desktop, Web, Mobile)  

We are hiring a highly skilled **AI Engineer & Full-Stack Developer** based in India, with a strong background in AI/ML, multi-agent architecture, and scalable, production-grade software development.

### Responsibilities:

- Build and maintain a multi-agent AI system (AutoGPT, BabyAGI, MetaGPT concepts)  

- Integrate large language models (GPT-4o, Claude, open-source LLMs)  

- Develop full-stack components (Backend: Python, FastAPI/Flask, Frontend: React/Next.js)  

- Work on real-time task execution pipelines  

- Build cross-platform apps using Electron or Flutter  

- Implement Redis, Vector databases, scalable APIs  

- Guide the architecture of autonomous, self-coding AI systems  

### Must-Have Skills:

- Python (advanced, AI applications)  

- AI/ML experience, including multi-agent orchestration  

- LLM integration knowledge  

- Full-stack development: React or Next.js  

- Redis, Vector Databases (e.g., Pinecone, FAISS)  

- Real-time applications (websockets, event-driven)  

- Cloud deployment (AWS, GCP)  

### Good to Have:

- Experience with code-generation AI models (Codex, GPT-4o coding abilities)  

- Microservices and secure system design  

- Knowledge of AI for workflow automation and productivity tools  

Join us to work on cutting-edge AI technology that builds the future of autonomous software.

Role & Responsibilities

Responsibilities

• Business: Immerse in operations until you think like an insider.

Rapidly acquire domain expertise through direct observation, translate between business and engineering seamlessly, and mentor engineers in your area on immersion. Influence senior stakeholders effectively, manage complex stakeholder landscapes with competing agendas, and build trust rapidly with new stakeholders.

• Delivery: Lead rapid delivery initiatives across teams in your area, coach on prototype-first approaches, and establish trust through consistent fast delivery. Build complete applications rapidly across any technology stack, select the right tools for each problem, and define clear criteria for prototype-to-production transitions.

• Generative AI: Architect RAG systems for complex use cases across teams, implement advanced techniques (hybrid search, reranking, query expansion), mentor engineers on RAG best practices, and establish RAG standards. Lead evaluation strategy across teams, establishing annotation guidelines, training human-calibrated LLM judges, and building evaluation pipelines that connect tracing to datasets to experiments.

• People: Build high-performing teams across your area, navigate complex interpersonal dynamics, foster psychological safety, and create environments where diverse perspectives are valued. Influence through communication at all levels — from frontline to executive. Handle difficult conversations skilfully and train engineers in your area on effective communication.

• AI-Augmented Development: Optimise AI tool usage across teams in your area, train engineers on AI-augmented and agentic engineering workflows, evaluate new AI development tools, and establish practices that balance AI speed with verification rigour.

• Scale: Design complex multi-component systems end-to-end, evaluate architectural options for large initiatives across teams, guide technical decisions for your area, and mentor engineers on architecture. Create debt reduction strategies across teams, influence roadmap decisions to include debt work, and teach engineers when to accept debt for speed versus when to invest in quality.

• Documentation: Define documentation standards across teams in your area, create documentation systems and templates, train engineers on spec-driven development, and ensure documentation quality across projects. Lead pattern generalization initiatives, defining criteria for when to generalize versus keep custom.

• Reliability: Define reliability standards across teams in your area, drive post-incident improvements systematically, design capacity planning processes, andmentor engineers on SRE practices.

Ideal Candidate

• Strong Staff Software Engineer / FDE profile (full-stack + production GenAI, multi-team technical leadership)
• Mandatory (Experience 1) – Must have 7+ years of relevant professional software engineering experience, with demonstrated full-stack delivery across backend and frontend.
• Mandatory (Experience 2) – Must have deep production experience with Python AND JavaScript/TypeScript, working comfortably across the full stack.
• Mandatory (Experience 3) – Must have 2+ years of experience in generative AI applications developement — LLM integrations, vector databases, RAG systems, and evaluation pipelines
• Mandatory (Experience 4) – Must have strong experience with modern frontend frameworks (Next.js / React) and backend API development.
• Mandatory (Experience 5) – Must have extensive experience with cloud platforms (AWS preferred; Azure/GCP valued), including infrastructure-as-code (CloudFormation / Terraform).
• Mandatory (Experience 6) – Must have working knowledge of multiple database paradigms — relational (PostgreSQL), document, and key-value (Redis) — with ability to select the right storage per problem.
• Mandatory (Experience 7) – Must have strong experience with CI/CD pipelines (e.g. GitHub Actions), containerization, and production deployment strategies.
• Mandatory (Experience 8) – Must have demonstrable fluency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) and proven ability to design agentic engineering workflows and train teams on them
• Preferred (Experience) – Advanced RAG techniques — hybrid search, reranking, query expansion — and establishing RAG standards across teams