Lead Architect – AI Systems & Cloud Infrastructure

Company: Humigent AI
Location: Gurgaon
Experience: 10+ years
Role Type: Full-Time | Leadership Track
Domains: Agentic AI, Multi-Cloud DevOps, Knowledge Graphs, Scalable Enterprise Applications

Role Overview

The Senior Architect – DevOps & Full-Stack will:

  • Own the architecture, scalability, reliability, and security of Humigent AI’s applications and agentic systems
  • Lead multi-cloud deployment across AWS + Azure
  • Architect knowledge-graph-driven AI applications
  • Mentor engineering teams and build a world-class technical foundation for all Humigent AI products

This role is a hands-on senior technical leadership position, combining architecture, DevOps excellence, full-stack engineering, and platform thinking.

Key Responsibilities

  1. Architecture Leadership
  • Define architecture for Agentic AI applications using LangGraph + LLMs + Knowledge Graphs
  • Design scalable microservices, event-driven systems, and multi-agent execution environments
  • Establish architecture patterns for ingestion, retrieval, reasoning, evaluation, and UI layers
  • Create end-to-end diagrams, design docs, standards, and reusable platform components
  1. Multi-Cloud DevOps & Infra (AWS + Azure)

Mandatory expertise in both clouds

  • Architect and manage production workloads on:
    • AWS: EC2, ECS/EKS, Lambda, S3, RDS, DynamoDB, API Gateway
    • Azure: App Service, Functions, Azure OpenAI, Blob Storage, Azure SQL/PostgreSQL
  • Build secure VPC/VNet architectures
  • Implement IaC using Terraform / CloudFormation
  • Establish logging, monitoring, and observability (CloudWatch, Azure Monitor, ELK, Prometheus, Grafana)
  • Create CI/CD pipelines with GitHub Actions and Docker
  • Lead performance engineering, autoscaling, load balancing, and failover strategies
  1. Full-Stack Engineering (Hands-On)
  • Architect and review backend systems using Python (FastAPI/Flask)
  • Drive front-end architecture using React/Next.js
  • Create high-performance APIs, reusable components, and scalable microservices
  • Own versioning, releases, code quality, and branching strategy
  1. Knowledge Graph + Ontology Architecture

Mandatory deep experience

  • Architect solutions using Neo4j, AWS Neptune, Azure Cosmos DB (Gremlin)
  • Define ontology schemas (OWL/JSON-LD), triple models, semantic layers
  • Build reasoning pipelines (GraphRAG, hybrid KG + LLM retrieval)
  • Integrate KGs with multi-agent systems for deterministic insights
  • Ensure graph performance, security, versioning, and governance
  1. LLM, RAG & Agentic Integration
  • Architect RAG pipelines with embeddings, vector DBs, caching, and re-ranking
  • Integrate with OpenAI, Azure OpenAI, AWS Bedrock
  • Build LangGraph, CrewAI and other Agentic AI based flows with custom tools and evaluators
  • Define memory strategies, state machines, and agent orchestration patterns
  1. Leadership, Mentorship & Process
  • Mentor full-stack, backend, DevOps, and agentic engineers
  • Establish coding guidelines, architecture reviews, and best practices
  • Collaborate with founders, product teams, domain experts, and clients
  • Translate business use cases into scalable system designs

Required Skills (Mandatory)

Technical Mastery

  • Python (FastAPI/Flask) + React/Next.js
  • AWS + Azure cloud architecture
  • Docker, Kubernetes, GitHub CI/CD
  • Knowledge Graph technologies (Neo4j/Neptune/CosmosDB)
  • Ontology modeling (OWL, RDF, JSON-LD)
  • Vector DBs: Pinecone, Qdrant, Chroma, Weaviate
  • LangGraph or CrewAI multi-agent system design
  • SQL mastery (PostgreSQL preferred)
  • Ingestion pipelines (structured + unstructured data)

Soft Skills

  • Strong ownership and architectural thinking
  • Excellent communication; ability to influence and guide teams
  • Ability to simplify complex concepts for business stakeholders
  • Calm under pressure; high accountability
  • Passion for AI, cognitive reasoning, and building world-class systems

Nice to Have

  • Experience in pharma, healthcare, food-tech domains
  • Experience with metric layers (DBT, semantic layers, metric stores)
  • Experience with time-series modeling and forecasting
  • Publications, open-source contributions, patents

    Apply to Humigent

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