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AI Engineer - Full Stack Developer
AI Engineer - Full Stack Developer

AI Engineer - Full Stack Developer at Marseer Ai · Remote only · 5 - 10 years · ₹20L - ₹30L / yr · Profitable · Remote only · Posted 14 Jun 2026

Marseer Ai's logo

AI Engineer - Full Stack Developer

Pragnya Chole's profile picture
Posted by Pragnya Chole
5 - 10 yrs
₹20L - ₹30L / yr
Remote only
Skills
skill iconPython
skill iconReact.js
skill iconNextJs (Next.js)
LangChain
FastAPI
skill iconNodeJS (Node.js)
TypeScript
Artificial Intelligence (AI)
OpenAI API
Retrieval Augmented Generation (RAG)
Vector database

About Marseer AI

Marseer AI (www.marseerai.com) is a Seattle-based company building an AI-powered marketing intelligence platform for DTC and retail e-commerce brands. The platform combines brand strategy, customer data, automation, and generative AI to help marketing teams drive consistent, data-driven engagement across email, SMS, paid media, SEO, and affiliate channels.


Role Overview

We are looking for an experienced AI Engineer - Full Stack Developer to join the Marseer platform team. This is a dual-track role: you will build and maintain AI agent pipelines, LLM integrations, and RAG-based intelligence systems on the backend, while also owning frontend interfaces that surface insights, recommendations, and campaign outputs to marketing teams and brand operators.


You should be equally comfortable designing multi-step agentic workflows in Python and building clean, responsive product interfaces in React/Next.js. You understand how LLMs behave in production, know how to engineer prompts and tool chains for reliability, and care deeply about the end-to-end user experience.


What You Will Do

- Design and implement multi-step AI agent workflows using LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar.

- Build and maintain RAG pipelines, including chunking strategies, embedding generation, vector store management, and retrieval tuning.

- Integrate with LLM providers such as OpenAI, Anthropic, or others, including prompt engineering, tool/function calling, structured output generation, and context window management.

- Develop AI-driven features such as campaign brief generation, audience recommendations, content variant creation, and performance insight summarization.

- Implement evaluation and observability frameworks to monitor LLM output quality, latency, and cost in production.

- Build frontend interfaces using React and Next.js, including dashboards, agent interaction UIs, campaign builders, and insight surfaces.

- Design and implement RESTful and/or GraphQL APIs in Python (FastAPI or Flask) or Node.js to serve AI outputs to the frontend.

- Integrate frontend with backend AI services, streaming LLM responses, and real-time status updates.

- Work with structured and unstructured marketing data, campaign performance metrics, audience segments, content libraries, and brand strategy documents.

- Integrate with marketing platforms and data sources such as Klaviyo, Google Ads, Meta, and Shopify.


Requirements

- 5-10 years of professional software engineering experience, with meaningful time in both backend and frontend development.

- Proven experience building and deploying LLM-powered applications in production, not just prototypes.

- Strong proficiency in Python for backend and AI development.

- Strong proficiency in React and Next.js for frontend development.

- Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or equivalent.

- Experience building RAG pipelines, vector stores such as Pinecone, Weaviate, pgvector, or similar, embedding models, and retrieval strategies.

- Experience with API design, REST or GraphQL, and backend service architecture.


Strongly Preferred

- Experience designing and building multi-agent or agentic AI systems with tool use, memory, and planning capabilities.

- Familiarity with prompt engineering best practices, structured output generation, and LLM evaluation methodologies.

- Experience with streaming LLM responses and real-time UI updates using SSE or WebSockets.

- Prior work in a SaaS product company shipping production features.

- Familiarity with marketing platforms, e-commerce data, or martech ecosystems.


Good to Have

- TypeScript and modern frontend tooling such as Tailwind CSS or shadcn/ui.

- Familiarity with Snowflake or other cloud data warehouses as data sources for AI pipelines.

- Experience with observability tools for LLM applications such as LangSmith, Helicone, Arize, or similar.

- Understanding of marketing concepts such as segmentation, campaign lifecycle, attribution, and content personalization.


What We Are Looking For

- Availability to work US business hours; overlap with US Eastern or Pacific timezone is required for client collaboration and team standups.

- Strong written and verbal English communication.

- Product sense and ownership mindset.

- Comfort with ambiguity in non-deterministic LLM-powered systems.

- Collaborative working style across engineering, design, and client-facing functions.


What We Offer

- Competitive compensation based on experience.

- Fully remote role; work from anywhere in India, with Hyderabad-based candidates preferred.

- High-impact work at the frontier of applied AI for marketing and e-commerce.

- Direct exposure to real brand problems, real data, and real production AI systems.

- A small, senior team where your architecture decisions matter and your contributions are visible.

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About Marseer Ai

Founded :
2024
Type :
Product
Size
Stage :
Profitable

About

Marseer AI deploys AI agents across email, paid social, SEO, affiliate, and creator marketing—so lean DTC teams can execute with enterprise-level sophistication.

Read more

Candid answers by the company

What is the location preference of jobs?

Fully remote, India based. Hyderabad Candidates preferred.

Company social profiles

linkedin

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Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.

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Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.

Solid understanding of RAG architectures, embedding models, and semantic search.

Experience with vector databases and similarity search infrastructure.

Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.

Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.

Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

Experience with multi-agent orchestration and agent memory systems (short-term and long-term).

Familiarity with fine-tuning or RLHF workflows for domain adaptation.

Background in NLP, information retrieval, or conversational AI.

Prior experience in B2B SaaS or CRM domain is a plus.

Contributions to open-source AI/ML projects or published research/blogs.

Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services

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Beyond Technologies
Posted by Beyond Technologies
Remote only
3 - 6 yrs
₹12L - ₹18L / yr
skill iconPython
Agentic AI

Job Title: Full Stack AI Engineer

Location: Remote/Hyderabad

Experience Level: 3-5

Salary Range: 12-18LPA

Application Link:https://beyond.ciltriq.com/apply/BUILD


Description:

Join a team building AI-powered systems that solve complex business problems and automate operational workflows across document processing, voice agents, enterprise integrations, workflow automation, and multi-agent systems.


Strong full-stack foundations: frontend state management, asynchronous user experiences and performance; backend API design, authentication, data modelling, databases, queues and distributed systems.


Strong coding ability in Python and JavaScript or TypeScript, with practical experience in modern frontend frameworks and backend services.


Requirements:

- Design and build complete systems: frontend applications, backend services, APIs, databases, data pipelines and integrations with customer systems.

- Build multi-agent workflows with clear agent responsibilities, tool access, shared state, context management, routing, handoffs and coordination across sequential and parallel tasks.

- Make agent execution dependable through durable state, checkpoints, retries, timeouts, idempotency, recovery and human approval or review where needed.

- Deliver document-processing pipelines, voice agents and retrieval-based AI applications, connecting model outputs to useful actions in real business workflows.

- Own quality in production: automated tests, AI evaluations, guardrails, observability, access controls, deployments, incident response and clear documentation.

- Choose where AI adds value and where deterministic software is the better fit. Balance accuracy, latency, cost, security and maintainability.

- Improve reusable engineering foundations, review code and help other engineers grow as the team expands.

- A solid understanding of tool calling, structured outputs, retrieval, context and memory management, model selection and evaluation.

- Practical cloud and deployment experience, including containers, CI/CD, secrets management, logging, monitoring and production debugging.

- Ability to reason from first principles, investigate failures across system boundaries and communicate technical decisions clearly to customers and teammates.

- Useful additional experience: Document AI and OCR, real-time voice systems, enterprise integrations, agent protocols such as MCP, and orchestration frameworks.

- Useful additional experience: Mentoring engineers or building reusable platforms.

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