Backend Engineer with 5+ years of experience building scalable, distributed systems and evolving them with AI capabilities.

I focus on designing high-throughput, reliable systems and integrating LLMs where they add real value—balancing performance, cost, and system complexity.

• Design and scale microservices & event-driven architectures
• Build fault-tolerant systems (idempotency, retries, backpressure, consistency)
• Architect data & async pipelines for high-scale workloads
• Integrate LLMs into backend systems (RAG, orchestration, AI workflows)
• Optimize latency, throughput, and cost across services

• Distributed systems & system design at scale
• Event-driven architecture & async processing
• Data-intensive systems & storage trade-offs
• AI system integration (RAG, agents, evaluation)
• Observability (metrics, logs, tracing, system health)

Languages
JavaScript (Node.js) · TypeScript · Go · Python

Data & Storage
PostgreSQL · MongoDB · Redis · TimescaleDB · ScyllaDB · Elasticsearch

Messaging & APIs
REST · GraphQL · gRPC · WebSockets · RabbitMQ

Infrastructure
AWS (Lambda, EC2, S3, DynamoDB, CloudWatch) · Docker · Kubernetes · CI/CD

AI Layer
LLM APIs · Embeddings · Vector Search · Retrieval Pipelines

• Designing AI-augmented backend systems
• Improving retrieval quality & evaluation in RAG pipelines
• Building observable, production-ready AI services
• Exploring scalable system patterns for AI workloads

• RAG System (v1 → v2 evolution)

  • Shifted from chat-based flow → retrieval-first architecture
  • Decoupled ingestion, indexing, and query pipelines
  • Focused on response quality, grounding, and system performance
  • https://github.com/abhishekguha95/chat-with-pdf

• learn-nodejs

  • Deep dive into Node.js internals & backend best practices
  • https://github.com/abhishekguha95/learn-nodejs

I’m interested in roles involving backend architecture, distributed systems, and AI integration at scale.

🔗 https://www.linkedin.com/in/abhishekguha1995/