Company: Cosentus

Experience: 3–5 Years

Employment Type: Full-Time

About the Role

Cosentus is looking for a Senior Full Stack & AI Platform Engineer with strong expertise in system design, scalable architecture, microservices, full-stack development, and AI-powered applications .

We are looking for a hands-on engineer who can independently design complex systems, develop application modules, integrate modern AI technologies, and take complete ownership from architecture through production.

A key challenge involves building scalable AI-powered healthcare applications capable of processing 2,000–5,000 medical charts within a 1–2 hour peak processing window , ensuring performance, reliability, accuracy, and cost efficiency.

The ideal candidate should be strong in Node.js, React.js, PostgreSQL, AWS Lambda, Amazon Bedrock, microservices, distributed systems, OCR, RAG, and practical AI/LLM implementation .

Key Responsibilities

- Design and develop scalable applications using React.js, Node.js, Express.js, TypeScript, PostgreSQL, and MongoDB .
- Own High-Level Design (HLD) and Low-Level Design (LLD) for complex modules, microservices, and distributed systems.
- Design high-throughput architectures using asynchronous processing, event-driven workflows, queues, caching, and parallel execution .
- Build and optimize AWS Lambda-based services and integrate AI models using Amazon Bedrock .
- Develop and scale AI/LLM workflows, OCR pipelines, Vision AI, structured data extraction, and RAG systems .
- Design scalable APIs, database schemas, background processing workflows, and communication between microservices.
- Implement concurrency controls, rate limiting, retries, idempotency, fault tolerance, and monitoring .
- Integrate AI agents, tool calling, and workflow orchestration where required.
- Identify and resolve performance bottlenecks across backend services, databases, AI models, and infrastructure.
- Take ownership of development, deployment, performance optimization, and production troubleshooting .

Core Technical Skills1. Full Stack Development & System Design Strong hands-on experience with React.js, Node.js, Express.js, JavaScript, TypeScript, PostgreSQL, MongoDB, and REST APIs .

Strong understanding of

- HLD, LLD, scalable architecture, and microservices
- Distributed systems, event-driven architecture, and asynchronous processing
- Database design, query optimization,



and API architecture
- Concurrency, caching, load balancing, and horizontal scaling
- Authentication, authorization, security, and fault tolerance
- Performance optimization and architectural trade-offs

The candidate must be capable of converting business requirements into detailed, scalable technical designs and implementing them independently .

1. Microservices, Distributed Systems & AWS

Strong practical knowledge of microservices, distributed processing, asynchronous workflows, and event-driven system architecture . Hands-on expertise with:

- AWS Lambda: Serverless development, deployment, concurrency, performance optimization, and scaling.
- Amazon Bedrock: Foundation model integration and production AI workflows.
- Working knowledge of AWS S3, SQS, API Gateway, CloudWatch, and IAM .

Experience with Redis, RabbitMQ, Kafka, or similar messaging and caching technologies . Understanding of worker concurrency, queue-based processing, retries, idempotency, rate limiting, failure recovery, observability, and cloud cost optimization .

Deep expertise across every AWS service is not required.

1. AI / LLM Engineering, OCR & Document Intelligence

Hands-on experience implementing production AI applications involving:
- LLM integration, prompt engineering, and context management.
- OCR, Vision AI, multimodal models, and PDF/image processing .
- Document classification, table/form extraction, and structured JSON outputs.
- Schema validation, error handling, and hallucination reduction.
- Multi-stage AI processing pipelines and model orchestration.
- Token optimization, AI latency management, and cost reduction.
- AI evaluation, accuracy improvement, and human-in-the-loop workflows.

The candidate should understand how to build reliable AI features beyond simply making LLM API calls .

1. RAG & AI Workflow Architecture

Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) systems, including:

- Document ingestion, chunking, and embeddings




- Vector databases, semantic/hybrid search, and metadata filtering
- Retrieval optimization, reranking, and context construction
- Response grounding and RAG evaluation

Working knowledge of AI agents, function/tool calling, agent orchestration, state management, and contextual memory . Experience with LangChain, LangGraph, or similar frameworks is preferred.

1. Scalability, Performance & Engineering Ownership

The candidate should be capable of:
- Designing architectures for high-volume document and AI processing.
- Performing capacity planning, throughput estimation, and load testing.
- Building scalable background workers and asynchronous pipelines.
- Optimizing APIs, databases, model calls, and infrastructure.
- Troubleshooting complex production issues.
- Making architectural decisions and explaining technical trade-offs.
- Independently owning complex technical modules from requirements to deployment.

Good to Have
- Experience with US Healthcare, Revenue Cycle Management (RCM), medical coding, insurance claims, or HIPAA-aware applications .
- Advanced AI agents, multi-agent workflows, and agent orchestration.
- Knowledge graphs, graph-based retrieval, and advanced AI memory.
- Docker, CI/CD pipelines, and containerized deployments.
- Experience with AI evaluation frameworks and feedback-driven improvement systems.

Experience Required
- 3–5 years of professional software development experience .
- Strong full-stack development experience, particularly in backend engineering.
- Strong system design, microservices, and scalable architecture knowledge.
- Hands-on experience with AWS Lambda and Amazon Bedrock .
- Practical experience with AI/LLM integration, OCR, and RAG .
- Ability to independently design, implement, and own complex technical modules.

Ideal Candidate We are looking for a hands-on engineer and robust system thinker who can take ownership of architecture, development, AI integration, scalability, and production reliability.

The ideal candidate should be able to design distributed architectures, implement production-ready modules, optimize high-volume AI processing workflows, and solve complex engineering challenges independently.

- Our highest priorities are system design, scalable backend architecture, microservices, AWS Lambda, and practical AI implementation.

📌 Platform Engineer — Healthcare AI Platform USA (New Delhi)
🏢 Cosentus
📍 New Delhi