Generative AI Engineer - Fullstack

Evnek Technologies Pvt Ltd; Bengaluru; On-site; INR 350,000 - 600,000; Full time

Job summary

Evnek Technologies Pvt Ltd is seeking a hands-on Generative AI Engineer with strong Full Stack experience to design and deploy production-grade AI applications. You will own the solution from backend to frontend, integrating LLMs, building RAG pipelines, and ensuring scalable delivery on cloud platforms like Azure, AWS, or GCP.

Collaboration with product and data teams is essential to deliver enterprise-ready GenAI solutions.

Qualifications

• 4–12 years of software engineering with hands-on GenAI development experience.
• Strong Python programming skills.
• Hands-on experience with Generative AI, LLMs, RAG, Prompt Engineering, and AI Agents.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent.
• Strong backend development experience with FastAPI/Flask and REST APIs.
• Experience with SQL and NoSQL databases.
• Hands-on knowledge of Vector Databases and Embeddings.
• Experience with at least one major cloud platform: Azure, AWS, or GCP.
• Knowledge of Git, Docker, CI/CD, and microservices architecture.
• Strong debugging, analytical, and problem-solving skills.

Responsibilities

• Design, develop, and deploy end-to-end Generative AI applications for enterprise use cases.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
• Develop LLM workflows, AI Agents, and multi-agent solutions.
• Integrate LLMs such as OpenAI/Azure OpenAI, Claude, Gemini, or open-source models into enterprise applications.
• Implement prompt engineering, function/tool calling, structured outputs, and LLM evaluation techniques.
• Develop scalable backend services and REST APIs using Python, FastAPI, or Flask.
• Build responsive frontend applications using React.js, Next.js, Angular, or similar frameworks.
• Integrate GenAI solutions with enterprise applications, APIs, databases, and third-party platforms.
• Work with Vector Databases such as Pinecone, FAISS, Chroma, Weaviate, or Azure AI Search.
• Implement document ingestion, chunking, embedding, retrieval, reranking, and response-generation pipelines.
• Deploy and manage AI applications on Azure, AWS, or GCP.
• Implement authentication, security, logging, monitoring, and performance optimization.
• Work closely with Product, Data, AI, and business teams to translate requirements into production-ready solutions.
• Troubleshoot production issues and continuously improve AI application quality, latency, accuracy, and scalability.

Skills

Python

Generative AI

LLMs

RAG

AI Agents

LangChain

LangGraph

LlamaIndex

Semantic Kernel

FastAPI

Flask

REST APIs

SQL

NoSQL

Vector Databases

Embeddings

Azure

AWS

GCP

Git

Docker

CI/CD

Microservices

Debugging

Analytical Skills

Kubernetes

Tools

LangChain

LangGraph

LlamaIndex

Semantic Kernel

FastAPI

Flask

React.js

Next.js

Angular

Azure

AWS

GCP

Git

Docker

CI/CD

Kubernetes

Vector Databases

Job description

We are looking for a hands-on Generative AI Engineer with strong Full Stack development experience to design, develop, and deploy production-grade AI applications. The ideal candidate should have expertise in LLMs, RAG, AI Agents, Python, APIs, modern frontend frameworks, databases, and cloud technologies.

The role requires an engineer who can take complete ownership of AI applications—from solution design and backend development to frontend integration, deployment, and production support.

Key Responsibilities

• Design, develop, and deploy end-to-end Generative AI applications for enterprise use cases.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
• Develop LLM workflows, AI Agents, and multi-agent solutions.
• Integrate LLMs such as OpenAI/Azure OpenAI, Claude, Gemini, or open-source models into enterprise applications.
• Implement prompt engineering, function/tool calling, structured outputs, and LLM evaluation techniques.
• Develop scalable backend services and REST APIs using Python, FastAPI, or Flask.
• Build responsive frontend applications using React.js, Next.js, Angular, or similar frameworks.
• Integrate GenAI solutions with enterprise applications, APIs, databases, and third-party platforms.
• Work with Vector Databases such as Pinecone, FAISS, Chroma, Weaviate, or Azure AI Search.
• Implement document ingestion, chunking, embedding, retrieval, reranking, and response-generation pipelines.
• Deploy and manage AI applications on Azure, AWS, or GCP.
• Implement authentication, security, logging, monitoring, and performance optimization.
• Work closely with Product, Data, AI, and business teams to translate requirements into production-ready solutions.
• Troubleshoot production issues and continuously improve AI application quality, latency, accuracy, and scalability.

Must-Have Skills

• 4–12 years of overall software engineering experience with hands-on GenAI development experience.
• Strong programming skills in Python.
• Hands-on experience with Generative AI, LLMs, RAG, Prompt Engineering, and AI Agents.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent.
• Strong backend development experience with FastAPI/Flask and REST APIs.
• Experience with SQL and NoSQL databases.
• Hands-on knowledge of Vector Databases and Embeddings.
• Experience with at least one major cloud platform: Azure, AWS, or GCP.
• Knowledge of Git, Docker, CI/CD, and microservices architecture.
• Strong debugging, analytical, and problem-solving skills.

Good to Have

Experience with Azure OpenAI, AWS Bedrock, or Google Vertex AI.

Knowledge of MLOps/LLMOps, model evaluation, monitoring, and guardrails.

Experience developing enterprise-grade AI Agents and Agentic AI workflows.

Exposure to Kubernetes and container orchestration.

Understanding of AI security, responsible AI, data privacy, and governance.

Experience delivering GenAI applications from POC to production.

Requirements

Ideal Candidate

We are looking for a hands-on GenAI Engineer who is also a strong Full Stack Developer and can independently build an AI solution from the backend and LLM layer through to the user interface and production deployment.