Job Description

Role: Full-Stack Developer (Python + React + Agentic AI)

Experience: 2-6 years

You will build complete AI-powered applications--from frontend screens to backend services, agents, LangGraph pipelines, knowledge graph reasoning, and cloud deployment on AWS and Azure.

Key Responsibilities1. Full-Stack Development (Mandatory)

Build production-grade applications using

FastAPI/Flask

and

React/Next.js

. Create enterprise dashboards, workflows, secure login systems (RBAC), and multi-screen interfaces. Develop modular UI components for complex AI tasks (generated insights, workflows, exports, reviews).
2. Agentic AI Integration (Mandatory)

Work with multi-agent frameworks such as

LangGraph

,

CrewAI

, or custom orchestrators. Implement LLM tools, memory systems, evaluator agents, and deterministic workflows. Build RAG pipelines, embeddings, vector search, and tool-based agent orchestration.
3. Knowledge Graph + Ontology Engineering (Mandatory)

Hands-on experience required in at least two of the following:

Graph databases:

Neo4j

,

Azure Cosmos DB (Gremlin)

,

AWS Neptune

Ontology design (OWL/JSON-LD), schema creation, triples, relationships Creating graph-based reasoning flows (GraphRAG, entity grounding, relationship inference) Integrating knowledge graphs with LLMs for retrieval + reasoning Designing semantic layers / metric definitions linked to graph schemas
This is a mandatory capability, as all Humigent systems rely on ontology-driven reasoning.

4. Backend Engineering & APIs (Mandatory)

Build secure microservices and APIs in Python (FastAPI/Flask). Strong SQL (PostgreSQL/MySQL), indexing, query tuning. Implement PDF/document/Excel ingestion pipelines. Build async flows, background tasks (Celery/Redis/RabbitMQ).
5. Cloud Engineering - AWS + Azure (Mandatory)

You must be comfortable deploying and managing workloads on both platforms.

AWS (Mandatory)

EC2, ECS/EKS, Lambda API Gateway, S3, RDS, DynamoDB IAM, VPC, Secrets Manager CloudWatch, CloudFormation/Terraform preferred
Azure (Mandatory)

Azure App Service, Azure Functions Azure OpenAI Azure Blob Storage, Azure SQL/PostgreSQL Virtual Machines, VNet, Key Vault Azure Monitor, Workbooks, Pipelines
DevOps (Mandatory)

Docker, GitHub Actions CI/CD pipelines Secure secrets, environment strategy, logging & monitoring
6. AI/ML & Data Engineering Skills

Integrate OpenAI, Azure OpenAI, Amazon Bedrock LLMs. Use vector DBs: Pinecone, Chroma, Qdrant, Weaviate. Familiarity with embeddings, token optimization, semantic caching. ETL skills for structured/unstructured data (PDF, tables, Excel, APIs).
Required Skills (Mandatory)Strong Hands-On

Python + FastAPI/Flask JavaScript/TypeScript + React/Next.js Agentic AI (LangGraph/CrewAI) Knowledge Graphs + Ontology Engineering Vector DB + RAG implementation AWS + Azure cloud deployments SQL (PostgreSQL preferred) Docker, CI/CD, GitHub Clean code, modular architecture, scalability
Soft Skills

Ownership mindset Strong analytical thinking Product-first approach Ability to learn quickly Collaboration & communication skills Passion for AI + problem solving
Nice-to-Have (Bonus)

Experience with pharma or food-tech datasets (TRx/NRx, FSSAI ingredients) Domain experience with clinical data or FDA PDFs Experience with cognitive science-based modeling GraphRAG, Knowledge Graph visualization, Ontologies like OBI/SNOMED Data visualization frameworks (Plotly, ECharts, D3.js) Working on high-scale SaaS enterprise applications
Job Type: Full-time

Work Location: In person

Job Type: Full-time

Pay: From ?386,481.98 per year

Work Location: In person