Responsibilities
• Define technical requirements with product, business, and engineering stakeholders
• Design, develop, and maintain production-grade Python applications and backend services
• Build asynchronous, API-driven services using technologies such as FastAPI and asyncio
• Design scalable APIs and microservices for internal and external consumers
• Own solutions from design through implementation, testing, deployment, monitoring, and production support
• Design for reliability, scalability, maintainability, and fault tolerance
• Apply distributed-system patterns including caching, queues, event-driven processing, and background workloads
• Diagnose production issues using logs, metrics, tracing, profiling, and other observability tools
• Participate in code reviews, contribute to engineering standards, and best practices
• Collaborate with engineering, QA, product, design, and business teams to deliver high-quality releases
• Maintain clear technical documentation for architectures, APIs, and workflows
• Use AI-assisted development tools to improve engineering productivity and code quality Applied AI / LLM Focus
• Build and productionize LLM-enabled capabilities such as classification, extraction, intelligent routing, workflow automation, and conversational experiences
• Integrate commercial and open-source LLM APIs into reliable production services
• Build tool/function-calling interfaces that safely connect AI systems with backend services and business workflows
• Design multi-step AI workflows with state management, validation, retries, fallbacks, and deterministic application logic
• Use structured outputs and validation to make LLM behavior predictable and testable
• Implement retrieval and ranking approaches such as embeddings, vector search, semantic retrieval,



re-ranking, and RAG where appropriate
• Build evaluation frameworks using representative datasets, regression tests, quality metrics, and production feedback
• Optimize AI systems for latency, reliability, cost, and quality
• Implement appropriate guardrails, observability, data controls, and auditability
• Evaluate when an LLM is appropriate versus deterministic logic, classifiers, or traditional software approaches

Requirements:
• 7+ years of professional software engineering experience with significant hands-on Python development in production
• Strong Python fundamentals, including data structures, exception handling, typing, testing, and maintainable application design
• Strong understanding of asynchronous and concurrent Python, including async/await, event-loop behavior, task management, timeouts, and blocking operations
• Experience building production APIs and microservices using FastAPI, Flask, Django, or similar frameworks
• Strong understanding of REST APIs, service integrations, authentication/authorization, and secure API design
• Experience designing and operating distributed backend systems using patterns such as queues, caching, pub/sub, retries, idempotency, and fault tolerance
• Ability to troubleshoot complex production issues across application code, infrastructure, and external dependencies
• Experience with logging, metrics, tracing, alerting, automated testing, CI/CD, and containerized deployments
• Strong familiarity with Git and modern code-review practices
• Ability to independently own significant technical work from problem definition through production
• Robust written and verbal communication skills and the ability to explain technical decisions and tradeoffs
• Ability to work effectively in a collaborative, cross-functional environment

📌 Python Developer (Bengaluru)
🏢 ACL Digital
📍 Bengaluru