Senior Python Engineer - AI/ML & Automation

Role Overview

We are looking for a strong Senior Python Engineer with hands-on AI/ML and Automation experience to join our engineering team.

The engineer will work on high-scale backend systems, intelligent automation platforms, OCR/document processing, AI-powered workflows, browser automation and integrations across multiple products.

This is not a role for someone whose AI experience is limited to calling LLM APIs. We need an engineer who understands Python deeply and has built, deployed and maintained real production systems.

Experience Requirements

• Minimum 4 years of professional software development experience, primarily using Python.
• Minimum 2 years of practical hands-on experience in AI/ML, intelligent automation or closely related production systems. Candidates with 4-6+ years of strong relevant experience are preferred.
• Strong production/backend engineering experience is essential.

Core Python & Backend Skills

• Python; FastAPI / Django / Flask; REST APIs and backend service development.
• Async programming and concurrency; multithreading / multiprocessing where appropriate.
• PostgreSQL / MySQL; Redis; background jobs and task queues such as Celery.
• WebSockets / real-time communication where required; authentication and authorization; API integrations.
• Docker; Linux; Git; logging, debugging and production troubleshooting.
• Ability to build fast, reliable and scalable Python services rather than only scripts.

AI / ML Requirements

• Machine Learning fundamentals; model training, evaluation and inference; data preprocessing and feature engineering.
• Scikit-learn; PyTorch or TensorFlow; NLP.
• Embeddings, semantic search and vector databases.
• LLM integrations, prompt engineering, structured output and tool/function calling.
• RAG pipelines; AI agents / agentic workflows.
• OpenAI, Gemini, Claude or equivalent integrations; local/open-source model integration where appropriate.
• Understanding of model selection, accuracy, latency and cost trade-offs.

Automation Engineering

• Playwright; Puppeteer or equivalent browser automation frameworks; Selenium where appropriate.
• Headless browser and web workflow automation; form/repetitive task automation.
• Session/cookie management; file upload/download automation; API + browser hybrid automation.
• Retry and recovery mechanisms; scheduling and background execution.
• Queue-based automation workers; parallel/concurrent task processing.
• Handling failures, timeouts and unexpected webpage changes.
• Automation should be designed for production reliability and scale, not just one-off scripts.

OCR / Document & Image Processing

• OCR pipelines; OpenCV; image preprocessing; document/image classification.
• Extracting structured information from screenshots, receipts, transaction slips and documents.
• Confidence scoring and validation; combining OCR with AI/ML models.
• Handling large-volume image/document processing.

Distributed & High-Scale Systems

• Microservices; message queues; RabbitMQ / Kafka or similar technologies.
• Redis; worker architecture; event-driven processing; horizontal scaling; caching.
• Rate limiting; idempotency; retry / exponential backoff; fault-tolerant processing.
• Database performance and API performance optimization.
• Experience with systems processing large numbers of concurrent jobs or users is preferred.

Production Engineering

• Dockerized applications; CI/CD fundamentals; AWS or equivalent cloud environments.
• Monitoring and observability; structured logging; metrics and alerting.
• Performance profiling; debugging production issues; automated tests.
• Secure handling of credentials, secrets and tokens.
• Deep DevOps expertise is not mandatory because we have a separate DevOps/SRE function, but production deployment and operational fundamentals are required.

Security Awareness

• Secure API development; authentication / authorization; encryption fundamentals.
• Secret management; input validation; OWASP fundamentals.
• Secure handling of customer and financial data; audit logging; role-based access controls.

Architecture & Engineering Quality

• Write clean, modular and maintainable code and follow appropriate design patterns.
• Participate in technical design discussions and design reusable automation components instead of duplicated scripts.
• Write unit/integration tests; perform code reviews; document important architecture and workflows.
• Identify performance bottlenecks and collaborate with Backend, Go, Java, DevOps/SRE and frontend engineers.
• The engineer does not need to be a Technical Architect, but should independently design a Python service or automation subsystem.

Good to Have

• Computer Vision; advanced OCR; ML model serving; MLOps; GPU-based inference; ONNX.
• Hugging Face; LangChain / LangGraph or similar frameworks.
• Vector databases such as Qdrant, Pinecone, Weaviate or Milvus.
• Kubernetes; Kafka; large-scale web automation; real-time systems; financial/transaction processing systems.

What We Will Evaluate During Interview

• Actual hands-on experience rather than resume keywords.
• Python depth; backend/API development; async/concurrency concepts; production debugging.
• Database and Redis knowledge; browser automation; Playwright/Puppeteer/Selenium experience.
• AI/ML fundamentals and actual AI/ML projects; OCR/image-processing experience.
• Queue/worker architecture; scalability and reliability thinking; system-design ability for Python services.
• Candidates should clearly explain what they personally designed, developed, deployed and troubleshot in production.

Ideal Candidate

The ideal candidate can independently take ownership of Python Backend + AI/ML + OCR + Automation + Production Integration and build reliable components capable of becoming part of large-scale production systems.

Pay: ₹1,350,000.00 - ₹2,000,000.00 per year

Work Location: In person