Full Stack AI Engineer

About the Role

We are looking for a Full Stack AI Engineer who can take an ambiguous problem and turn it into a complete, production-ready AI product.

This is a builder role .

You will work across the entire stack — AI models, agents, backend services, APIs, databases, data pipelines, frontend applications, infrastructure, and production deployment. You should be comfortable deciding what needs to be built, writing the code, deploying it, measuring whether it works, and continuously improving it.

We are not looking for someone who only builds notebooks, trains models, writes prompts, or creates architecture diagrams for another team to implement. We want engineers who ship complete products .

A typical project might involve designing an agentic workflow, building a retrieval pipeline, writing Python APIs, creating a React interface, integrating enterprise data, deploying to Kubernetes, implementing evaluations, and debugging the application in production.

The distance between an idea and working software should be measured in weeks, not quarters .

This role is based in Hyderabad and is 6 days per week in the office .

What You’ll Do

Build AI Products End-to-End

• Own AI applications from problem definition through architecture, development, deployment, and production operation.
• Translate ambiguous product and business requirements into working software.
• Build across AI/ML, backend services, APIs, databases, frontend interfaces, data pipelines, authentication, infrastructure, and observability.
• Rapidly prototype, test with real users and data, and turn successful ideas into production-grade systems.
• Make pragmatic engineering decisions based on speed, reliability, simplicity, maintainability, and user value.

AI, LLMs & Agents

• Build production applications using commercial and open-source foundation models.
• Design RAG systems, agentic workflows, tool/function calling, structured outputs, memory, human-in-the-loop workflows, and multi-agent systems where appropriate.
• Work with frameworks such as LangGraph, LangChain, Semantic Kernel, LlamaIndex , or equivalent tools.
• Build retrieval systems using embeddings, vector search, BM25, hybrid retrieval, reranking, metadata filtering, and knowledge graphs.
• Design prompt and context-engineering strategies for complex workflows.
• Evaluate model choices based on accuracy, latency, reliability, security, and cost.
• Build automated evaluations and regression tests for AI behavior.
• Fine-tune or adapt models when prompting and retrieval are insufficient.

Backend, Frontend & Data

• Build production backend systems primarily in Python using FastAPI, Flask, Django , or similar frameworks.
• Design APIs, asynchronous workflows, background jobs, queues, caching layers, and event-driven systems.
• Work with PostgreSQL, SQL Server, MongoDB, Redis, Snowflake , and other production data stores.
• Build modern applications using React, Next.js, TypeScript, JavaScript , or equivalent frameworks.
• Create interfaces for copilots, conversational AI, workflow automation, analytics, review queues, and operational applications.
• Implement streaming responses, real-time updates, authentication, permissions, and API integrations.
• Build ingestion and transformation pipelines for structured and unstructured enterprise data.
• Work with documents, databases, APIs, event streams, images, logs, and operational datasets.
• Maintain provenance, permissions, metadata, and traceability across enterprise information.

ML & Computer Vision

• Use classical ML or deep learning when it is better suited to the problem than an LLM.
• Build systems involving classification, forecasting, anomaly detection, ranking, recommendations, optimization, or prediction.
• Build computer-vision applications involving detection, classification, segmentation, OCR, tracking, or image/video analysis.
• Work with PyTorch, TensorFlow, Hugging Face, OpenCV , or equivalent tools.
• Understand model development, evaluation, inference, and productionization.

Deploy & Operate What You Build

• Deploy applications across AWS, Azure, GCP, on-premises, hybrid, or edge environments .
• Containerize and operate applications using Docker and Kubernetes .
• Build CI/CD pipelines, automated testing, monitoring, and observability.
• Own reliability, latency, availability, security, evaluation, cost, and scalabilit…