Humigent Ai

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 Pay: ₹388,525.00 - ₹1,627,207.67 per year Work Location: In person