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Fullstack_AI Engineer
Fullstack_AI Engineer

Fullstack_AI Engineer at Appiness Interactive · Pune · 4 - 7 years · ₹20L - ₹22L / yr · Posted 14 Jul 2026

Appiness Interactive's logo

Fullstack_AI Engineer

Archana M's profile picture
Posted by Archana M
4 - 7 yrs
₹20L - ₹22L / yr
Pune
Skills
skill iconPython
skill iconReact.js
Multi-agent Systems

We're Hiring | Full Stack AI Engineer – Multi-Agent Systems

We are looking for a Full Stack AI Engineer who can own end-to-end feature delivery – from designing agent communication patterns and orchestration logic on the backend, to building intuitive interfaces for configuring, monitoring, and debugging agent workflows. You will work directly with the architect and product owner to translate orchestration concepts (agent-to-agent messaging, tool use, handoffs, memory) into a production-grade system.

This is a hands-on builder role. You will not be writing strategy decks – you will be shipping agents, APIs, and UI.


Location: Pune

Experience: 4–7 Years (1.5+ years in GenAI/AI Agents)

Employment Type: Full-time


Key Responsibilities


Backend & Agent Development


•       Design and implement multi-agent workflows using AG2 (AutoGen) and/or LangGraph – including agent roles, conversation patterns, handoffs, and termination conditions.

•       Build and maintain FastAPI services that expose orchestration capabilities (agent registration, task submission, run status, streaming responses) to the frontend and external consumers.

•       Implement tool/function calling, structured outputs, and memory layers (short-term, long-term, vector-based) for agents.

•       Integrate LLM providers (OpenAI, Anthropic, AWS Bedrock, or local models via Ollama) with proper retry, timeout, and cost controls.

•       Write clean, testable Python with type hints, pydantic models, and clear separation between orchestration logic and infrastructure.


Frontend Development


•       Build React-based interfaces for configuring agents, visualising agent conversations, inspecting tool calls, and debugging runs.

•       Implement streaming UI (SSE/WebSockets) to render token-by-token agent responses and intermediate steps.

•       Collaborate on UX for workflow builders, run history, and observability dashboards.

System & Production Concerns

•       Containerise services with Docker and contribute to CI/CD pipelines.

•       Add logging, tracing, and basic evaluation hooks for agent runs (latency, token usage, success rates).

•       Participate in code reviews, design discussions, and incremental hardening of the platform.


Must-Have Skills


•       Full stack experience (5+ years): strong Python (FastAPI or similar) on the backend AND React on the frontend. Comfortable owning a feature across both layers.

•       Hands-on with at least one agent framework: AG2 (AutoGen), LangGraph, LangChain Agents, or CrewAI. Must have actually built and run multi-agent flows, not just read about them.

•       LLM application development: prompt design, tool/function calling, structured outputs, handling streaming responses, and basic context management.

•       RAG fundamentals: chunking strategies, embeddings, vector stores (pgvector / Qdrant / Weaviate / FAISS), and retrieval evaluation. Bonus for hybrid (sparse + dense) retrieval.

•       Database skills: PostgreSQL – schema design, indexing, and writing reasonable queries.

•       API & async patterns: REST design, async Python (asyncio), background jobs, and streaming endpoints.

•       Version control & collaboration: Git, PR-based workflows, writing clear commit messages and design notes.


Good-to-Have Skills


•       Experience with GCS for deploying AI workloads.

•       Familiarity with LLMOps – tracing (LangSmith / Langfuse / OpenTelemetry), evaluation frameworks, and prompt versioning.

•       Exposure to MCP (Model Context Protocol) or A2A (agent-to-agent) communication standards.

•       Experience running local LLMs (Ollama, vLLM) for prototyping or cost-sensitive workloads.

•       Knowledge of WebSockets / SSE for real-time UIs.

•       Basic understanding of fine-tuning concepts (LoRA / QLoRA) – we are not expecting trainers, just awareness.

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Full Stack

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  • Experience with React.js or similar frontend frameworks is preferred.
  • Ability to integrate frontend applications with Python/FastAPI services.
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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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Shubham Vishwakarma

Full Stack Developer - Averlon
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