Overview

Position: Workflow Automation (Temporal) + AI Lead

Experience: 8–15 Years

Location: Remote / Hybrid / Work From Office (India)

Preferred Location: Tamil Nadu & Bangalore Candidates Preferred

Base Location: Madurai, Tamil Nadu (Monthly travel to Madurai is mandatory)

Contract Duration: Long-Term

Shift Timing: 2:00 PM – 11:30 PM IST

Domain: Healthcare

Employment Type: Contract (C2C)

Notice Period: Immediate Joiners / Bench Resources (Bench Resource is Mandatory)

Linkedin of the candidate is mandatory

About the Role

We are looking for an experienced Workflow Automation (Temporal) + AI Lead to architect and develop enterprise-scale AI automation platforms for the Healthcare domain. The ideal candidate should have deep expertise in Python backend development, workflow orchestration using Temporal, AI Agent frameworks, cloud-native technologies, and enterprise integrations.

The candidate will lead the design and implementation of scalable AI-powered workflow automation solutions while mentoring engineering teams and collaborating with cross-functional stakeholders.

Key Responsibilities

• Design and develop enterprise-grade AI automation platforms.
• Build scalable backend services using Python (FastAPI/Flask/Django).
• Develop REST APIs, asynchronous services, and microservices.
• Design and implement long-running workflows using Temporal (preferred) or equivalent workflow orchestration platforms.
• Build AI Agents using modern frameworks such as CrewAI, AutoGen, Semantic Kernel, LangGraph, etc.
• Integrate LLMs using Amazon Bedrock, Azure OpenAI, or OpenAI.
• Integrate Python services with C/.NET applications and enterprise systems.
• Design secure, scalable, event-driven backend architectures.
• Implement authentication and authorization using OAuth2, JWT, SSO, RBAC/ABAC.
• Develop AI-powered Healthcare workflows covering:
  • Revenue Cycle Management (RCM)
  • Claims Processing
  • Enrollment
  • Eligibility
  • Prior Authorization
  • Provider/Payer Integrations
  • Intelligent Document Automation
• Build end-to-end Full Stack AI applications.
• Implement AI Ops practices including:
  • Monitoring
  • Logging
  • Observability
  • Model Lifecycle Management
  • Production Support
• Drive engineering best practices, CI/CD implementation, documentation, and mentor development teams.

Required Skills

• Backend Development
• Python (Expert)
• FastAPI / Flask / Django
• REST APIs
• WebSockets
• Async Programming
• Microservices Architecture
• External API Integrations
• C/.NET Integration
• Workflow Automation
• Temporal (Preferred)
• Workflow Orchestration
• Event-Driven Architecture
• Distributed Processing
• Long-Running Workflow Management
• Stateful & Stateless Services
• AI & Generative AI
• CrewAI
• AutoGen
• Semantic Kernel
• LangGraph
• Amazon Bedrock
• Azure OpenAI
• OpenAI APIs
• Prompt Engineering
• RAG Architecture
• Guardrails
• MCP (Model Context Protocol)
• AI Skills & Tooling
• Vector Databases
• ChromaDB
• Pinecone
• pgvector
• Security
• OAuth2
• JWT
• Single Sign-On (SSO)
• RBAC / ABAC
• API Security
• Secrets Management
• Cloud & DevOps
• AWS (Preferred) or Azure
• Docker
• Git
• CI/CD Pipelines
• Monitoring & Logging
• AI Ops
• Observability
• Production Deployment & Support
• Databases
• SQL Server
• PostgreSQL
• Amazon Redshift
• Redis
• NoSQL Databases
• Vector Databases

Preferred Skills

• Experience building enterprise AI Automation platforms.
• Strong Healthcare domain experience.
• Expertise in Revenue Cycle Management (RCM), Claims, Enrollment, Eligibility, Prior Authorization, or Provider/Payer workflows.
• Strong understanding of distributed systems and scalable backend architecture.
• Experience integrating AI solutions with enterprise applications.
• Excellent debugging and problem-solving skills.
• Experience leading engineering teams, conducting code reviews, and mentoring developers.
• Experience delivering production-grade Full Stack AI applications.

Nice to Have

• Kubernetes
• Kafka / RabbitMQ
• GraphQL
• Playwright / Browser Automation
• Terraform / Infrastructure as Code (IaC)
• Snowflake
• Databricks
• MLflow / Model Lifecycle Management
• React or Modern Frontend Frameworks
• Healthcare interoperability standards (FHIR/HL7)