Role: AI Engineer (Senior)
Year: 2026
Qualification: Not Specified
Experience (years): 1-5
Job description:
AI Agent Engineering
LangGraph or equivalent graph-based agent frameworks.
Multi-step reasoning pipelines.
Tool usage and orchestration.
State management and conversational workflows.
RAG & Vector Search
End-to-end RAG pipeline design and implementation.
Experience with vector databases such as:
Pinecone
Qdrant
pgvector
Weaviate
Chunking strategies and retrieval optimization.
Retrieval evaluation methodologies.
LLM Integration
OpenAI, Gemini, and Anthropic SDKs.
Prompt engineering and prompt optimization.
Structured JSON outputs.
Context window management.
Multi-provider LLM integrations.
Python Backend Development
Python 3.12
FastAPI
Async Python
Pydantic
SQLite
PostgreSQL
Redis
Pytest
Full-Stack Development
React
Next.js
TypeScript
Modern frontend architecture
API integration and state management
ML Engineering Fundamentals
Evaluation pipelines
Golden datasets and test suites
Regression tracking
Model performance monitoring
Good to Have
GIS & Mapping
ArcGIS REST APIs
GeoJSON
MapLibre GL JS
Spatial queries
(Strong advantage for initial project assignments.)
Data Visualization
Recharts
D3.js
Equivalent charting libraries
Cloud & DevOps
Docker
Azure
AWS
CI/CD pipelines
OIDC Authentication
Product Thinking
Ability to understand and interpret Figma designs.
Evaluate trade-offs between engineering effort and business value.
Deliver solutions aligned with business objectives.
Technologies You'll Work With
LayerTechnology Stack
Agent Frameworks
LangGraph, LangChain
LLM Providers
Gemini, OpenAI, Anthropic
Backend
Python 3.12, FastAPI, SQLite, Redis
Frontend
Next.js 15, React 19, TypeScript, Zustand
Data & Visualization
Recharts, GeoJSON, MapLibre GL JS
Infrastructure
Docker, Azure Pipelines, Azure AD 
Technical skills: Python, ML, LangChain, LangGraph, RAG, VectorDB, LLMs, APIs, Prompt Engineering, SQL, React, Javascript, Docker, CI/CD, AWS/Azure
Soft skills: Communication, Problem Solving