JOB ID:QT-SUL-08-432

Role Overview

We are seeking an experienced Senior AI / ML Engineer to design, build, and lead the development of enterprise-grade AI automation platforms for Healthcare and Revenue Cycle Management (RCM).

The ideal candidate will have solid expertise in Python backend development, workflow orchestration, AI agent frameworks, LLM integration, cloud-native architecture, enterprise integrations, and AI Ops.

This role will be responsible for developing scalable AI-driven solutions, integrating intelligent workflows with enterprise systems, driving engineering best practices, and mentoring engineers while collaborating closely with product, architecture, data, and business teams.

Key Responsibilities AI Platform & Solution Architecture

- Design and develop enterprise-grade AI automation platforms and intelligent workflows.
- Define scalable, secure, and reliable architectures for AI-powered applications.
- Design stateful and stateless backend services using microservices and event-driven architectures.
- Build reusable AI components, services, agents, and automation frameworks.
- Evaluate emerging AI technologies and frameworks and incorporate them into production solutions where appropriate.

Python Backend Engineering
- Develop scalable and high-performance backend services using Python.
- Build REST APIs using FastAPI, Flask, or Django.
- Implement asynchronous processing, WebSockets, background jobs, and distributed processing.
- Design and develop microservices and reusable backend components.
- Integrate Python services with C#/.NET applications, enterprise systems, and third-party APIs.
- Design robust error handling, retry mechanisms, logging, and fault-tolerant services.

Workflow Orchestration & Automation
- Design and implement complex business workflows using Temporal or equivalent workflow orchestration platforms.
- Build and manage long-running, distributed, and fault-tolerant workflows.
- Implement workflow state management, retries, timeouts, compensation, and recovery mechanisms.
- Design event-driven automation using messaging and distributed processing patterns.
- Optimize workflow execution for scalability, reliability, and operational efficiency.

Generative AI & Agentic AI
- Design and develop AI agents and intelligent automation solutions using modern agent frameworks.
- Work with frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalent.
- Integrate LLMs through Amazon Bedrock, OpenAI, Azure OpenAI, or similar platforms.
- Implement prompt engineering, structured outputs, tool calling, agent orchestration, and context management.
- Design and implement RAG architectures using embeddings and vector databases.
- Implement AI guardrails, validation, safety controls, and responsible AI practices.
- Develop integrations using MCP (Model Context Protocol) and AI tools/skills where applicable.
- Evaluate and optimize AI agent accuracy, reliability, latency, and cost.

Healthcare & RCM Automation




- Develop AI-powered workflows for Healthcare and Revenue Cycle Management (RCM).
- Build automation solutions supporting:
- Claims processing
- Eligibility verification
- Enrollment
- Prior Authorization
- Payer/Provider workflows
- Document automation
- Revenue cycle operations

- Integrate AI capabilities with healthcare enterprise applications and external systems.
- Work with healthcare interoperability standards such as FHIR / HL7 where applicable.

Security & Enterprise Integration
- Implement secure authentication and authorization mechanisms.
- Work with OAuth2, JWT, SSO, RBAC, and ABAC.
- Implement API security, access controls, encryption, and secrets management.
- Design secure integrations between AI services, enterprise applications, and external APIs.
- Ensure AI applications comply with enterprise security and governance requirements.

AI Ops, Observability & Productionization
- Implement AI Ops and MLOps practices for production AI applications.
- Build monitoring and observability for APIs, workflows, models, and AI agents.
- Implement centralized logging, metrics, tracing, alerting, and performance monitoring.
- Establish AI evaluation processes to monitor model and agent quality.
- Support model lifecycle management, deployment, versioning, and rollback strategies.
- Troubleshoot production issues and provide ongoing production support.
- Monitor AI system performance, reliability, latency, usage, and operational costs.

Engineering Leadership
- Drive engineering best practices, coding standards, architecture guidelines, and documentation.
- Conduct code reviews and provide technical guidance to engineering teams.
- Mentor junior and mid-level engineers.
- Collaborate with product managers, architects, data scientists, developers, QA, and business stakeholders.
- Contribute to CI/CD automation and reliable software delivery practices.
- Lead technical discussions, solution design, debugging, and root-cause analysis.

Required Skills Backend Engineering
- Expert-level Python
- FastAPI, Flask, or Django
- REST APIs and WebSockets
- Asynchronous programming
- Microservices architecture
- Distributed systems
- External API integrations
- C#/.NET integration experience

Workflow & Automation
- Temporal - Preferred
- Experience with equivalent workflow orchestration engines
- Event-driven architecture
- Distributed processing
- Long-running workflow management
- Workflow state management
- Retry, timeout, and failure recovery patterns

AI & Generative AI
- AI Agent frameworks such as:
- LangGraph
- CrewAI
- AutoGen
- Semantic Kernel





- LLM integration using Amazon Bedrock / OpenAI / Azure OpenAI
- Prompt Engineering
- Agent orchestration
- Tool calling and function calling
- Guardrails and AI safety
- RAG architecture
- Embeddings and semantic search
- Vector databases such as ChromaDB, Pinecone, or pgvector
- MCP (Model Context Protocol) and AI tooling

Security
- OAuth2
- JWT
- SSO
- RBAC / ABAC
- API Security
- Secrets Management
- Secure API and service-to-service communication

Cloud & AI Ops
- AWS - Preferred
- Azure
- Docker
- Git
- CI/CD pipelines
- Monitoring and logging
- Observability
- AI Ops / MLOps
- Model monitoring and evaluation
- Production deployment and support

Databases
- SQL Server
- PostgreSQL
- Amazon Redshift
- Redis
- NoSQL databases
- Vector databases

Preferred Qualifications
- Experience building enterprise AI automation platforms.
- Healthcare domain experience, particularly in RCM, claims, eligibility, enrollment, prior authorization, or payer/provider workflows.
- Strong understanding of distributed systems and scalable backend architecture.
- Experience integrating AI solutions with existing enterprise applications.
- Strong architecture, debugging, analytical, and problem-solving skills.
- Experience leading technical teams and mentoring engineers.
- Experience conducting code reviews and establishing engineering standards.
- Proven experience delivering production-grade Full Stack AI applications.

Nice to Have
- Kubernetes
- Kafka / RabbitMQ
- GraphQL
- Playwright / Browser Automation
- Terraform / Infrastructure as Code
- Snowflake
- Databricks
- MLflow
- Model Lifecycle Management
- React or modern frontend frameworks
- FHIR / HL7
- Healthcare interoperability
- Cloud-native application development

Key Skills

Must Have:

Python | FastAPI | Microservices | REST APIs | Async Programming | Temporal | AI Agents | LLM | Generative AI | RAG | Prompt Engineering | Guardrails | Vector Databases | AWS | Docker | CI/CD | AI Ops | Observability | OAuth2 | JWT | RBAC/ABAC

Good to Have:

LangGraph | CrewAI | AutoGen | Semantic Kernel | MCP | Amazon Bedrock | Kubernetes | Kafka | RabbitMQ | Terraform | MLflow | React | GraphQL | FHIR | HL7 | Snowflake | Databricks

Ideal Candidate

The ideal candidate is a hands-on Senior AI / ML Engineer who can work across Python backend engineering, AI agents, LLMs, workflow orchestration, cloud platforms, enterprise integrations, and production operations.

The candidate should be comfortable taking AI solutions from concept and prototype through architecture, development, deployment, monitoring, and production support, while also providing technical leadership and mentoring to engineering teams.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior AI / ML Engineer - AI Automation & Intelligent Workflows (Nagpur)
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📍 Nagpur