AI Native Software Engineer
Project Role Description : Build and ship production-ready software using AI as a core part of the engineering lifecycle. Apply AI-native practices to develop and validate solutions, maintain quality and compliance, and continuously improve how software is built and operated.
Must have skills : Python (Programming Language)
Good to have skills : Agentic Design
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary
Build and deploy production-grade AI applications using LLMs, RAG pipelines, and agentic systems
Work across backend (Python/FastAPI) and frontend (React/JavaScript) stacks
Deploy and operate AI services in cloud-native Kubernetes environment
Leverage modern GenAI frameworks and tooling to accelerate development
Key Responsibilities
Design and develop LLM-powered backend services using Python and FastAPI
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases
Implement agentic workflows using frameworks such as AutoGen or similar
Design and expose RESTful APIs following OpenAPI / REST API specifications
Integrate backend AI services with React + JavaScript frontend applications
Deploy, manage, and scale AI services on Kubernetes
Use LangChain to orchestrate prompts, tools, chains, memory, and agents
Use LangSmith for:
Prompt/version tracking
LLM tracing and debugging
Evaluation and performance monitoring
Apply prompt engineering best practices and maintain reusable prompt libraries
Collaborate with cross-functional teams to deliver end-to-end AI solutions
Ensure security, reliability, observability, and performance of AI systems
Required Technical Skills
Backend & APIs
Strong hands-on experience with:
Python
FastAPI
Experience designing and consuming RESTful APIs
Familiarity with OpenAPI / Swagger specifications
Frontend
Working knowledge of:
React
JavaScript
Experience integrating frontend applications with backend APIs
Generative AI & LLMs
Hands-on experience working with Large Language Models (LLMs)
Agentic system design patterns knowledge
Experience building RAG-based applications
Knowledge of:
Embeddings
Vector search
Context management
Practical experience with prompt engineering
GenAI/Agentic Frameworks & Tooling (Must Have)
LangChain
Chains, tools, agents, memory
LangSmith
Prompt tracing, debugging, evaluation, and monitoring
Agentic frameworks
AutoGen or similar
Experience with multi-agent orchestration, tool-calling agents, planners, and executors
Protocols like MCP / A2A
Cloud & DevOps
Experience with:
Docker
Kubernetes
Understanding of cloud setup and deployment (AWS / Azure / GCP)
Basic CI/CD knowledge is a plus
Gen AI Tools
AI productivity tools for general development:
GitHub Copilot
Claude / Cursor / Cline or similar
Good to Have/Differentiators
Working knowledge/experience with
Vector databases:
Pinecone, FAISS, Weaviate, Chroma, or equivalent
Graph databases:
Apache AGE, Neo4j etc.
Strong basics in Security/AuthN/AuthZ:
JWT, OAuth2
Working knowledge of monitoring and observability tools.
Soft Skills
Strong problem-solving and debugging skills
Ability to work independently and take ownership
Clear communication and collaboration skills
High curiosity for fast-evolving AI and GenAI ecosystems
Bengaluru
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