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 explicit 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
- Strong 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