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Lead Software Engineer

Summary

Lead a team to build AI-powered document processing systems using NLP, OCR, and LLMs on GCP, designing scalable pipelines and cloud-native services.

End Date

Sunday 23 August 2026

We Support Flexible Working – Click here for more information on flexible working options

Flexible Working Options

Hybrid Working

Job Description Summary

Minimum 10 years of Strong expertise in NLP, Document AI, and AI-driven data processing systems, Solid software engineering skills with focus on clean, scalable, and production-grade solutions, Hands-on experience with data pipelines and cloud-native platforms (GCP), Ability to design and deliver end-to-end AI-enabled solutions within team scope

Job Description

Experience: 9- 15 years
Location: Hyderabad
Job Type: Full Time


AI, NLP & Document AI (Primary Differentiator)

  • NLP fundamentals:
    • Text extraction, classification, entity recognition
  • Document AI:
    • OCR, document parsing, structured/unstructured data extraction
  • LLMs / GenAI:
    • Prompt engineering
    • Retrieval-Augmented Generation (RAG)
  • Knowledge of document workflows:
    • Input → processing → enrichment → output

Data Engineering & Processing (Tech & Data Arch)

  • Data pipelines (ETL/ELT)
  • Batch and streaming data processing
  • Handling large document datasets
  • SQL, BigQuery / data platforms
  • Data transformation and enrichment

Software Engineering Excellence (Core Expectation)

  • Strong programming skills (Python/Java)
  • Writing clean, efficient, maintainable code
  • Use of design patterns (API design, modularisation)
  • Code reviews and engineering best practices
  • Unit + integration testing

Cloud & Platform Engineering

  • Cloud platforms (GCP preferred):
    • BigQuery, Vertex AI, storage, compute
  • Containerisation (Docker)
  • Kubernetes basics
  • API-based services

DevOps & CI/CD

  • CI/CD pipelines (Jenkins, GitHub Actions, etc.)
  • Source control (Git)
  • Automated builds and deployments
  • Environment management

Reliability, Performance & Observability (Important)

  • Logging and monitoring basics
  • Performance tuning (latency of AI APIs, pipelines)
  • Understanding system failures and debugging
  • Awareness of scalability constraints

System & Solution Design (Team-Level)

  • Design small-to-medium systems
  • API-first design thinking
  • Integration of AI + data + services
  • Understanding trade-offs (performance vs cost vs complexity)

Collaboration & Delivery

  • Work with:
    • Product owners
    • Data scientists
    • Platform teams
  • Agile practices (stories, sprints, backlog)
  • Communicate technical solutions clearly

See also