Senior Python Software Engineer - Azure, Distributed Systems & AI (NYC)

We are not looking for data scientists, research-focused AI practitioners, or engineers whose primary experience is building ETL jobs, data ingestion frameworks, or data pipelines. We need a strong software engineer first-someone who writes maintainable Python, understands concurrency and distributed systems, and can independently troubleshoot issues in live environments.

We are looking for a hands-on Senior Python Software Engineer with deep core Python expertise and a proven track record of building, deploying, debugging, and operating production software on Microsoft Azure., Experience building AI-powered applications and integrating LLMs into production software is a plus, but strong software engineering fundamentals and production ownership are essential., * 8+ years of professional software engineering experience, primarily in backend development, application engineering, or distributed systems. \n

• Strong hands-on Python expertise, including application design, debugging, testing, performance optimization, and writing production-quality code. \n

• Demonstrated experience shipping and maintaining software that runs in production-not just prototypes, notebooks, scripts, or proof-of-concept applications. \n

• Real-world Microsoft Azure experience, including deploying and troubleshooting applications hosted in Azure production environments. \n

• Strong understanding of backend application architecture, APIs, databases, networking fundamentals, and distributed systems. \n

• Experience diagnosing production incidents, identifying root causes, implementing fixes, and preventing recurrence. \n

• Practical experience with automated testing, source control, CI/CD, monitoring, and safe software deployment practices. \n

• Ability to independently own technical problems, make sound engineering decisions, and communicate clearly during incident investigation and resolution. \n, * Experience operating Python applications on Azure Functions, App Service, Azure Container Apps, or AKS. \n

• Experience with FastAPI, Flask, Django, or similar Python application frameworks. \n

• Strong understanding of asyncio, asynchronous I/O, concurrency control, and performance profiling. \n

• Experience with Docker, Kubernetes, infrastructure configuration, and automated deployment workflows. \n

• Experience with relational or NoSQL databases, caching, messaging systems, and external service integrations. \n

• Experience shipping AI-enabled applications using LLM APIs, Azure OpenAI, RAG, or agent orchestration frameworks. \n

• Familiarity with Azure Monitor, Application Insights, Log Analytics, OpenTelemetry, or comparable observability tools. \n

• Working knowledge of C# or .NET is helpful but not required. \n, Candidates should be prepared to discuss real examples of Python applications they have shipped, their role in deploying and operating those applications, and challenging production issues they personally investigated., We will assess practical Python proficiency, debugging methodology, system design, Azure production experience, testing practices, and operational judgment., We are looking for engineers who can take ownership, work through ambiguity, and deliver software that remains reliable after it reaches production

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• Design, develop, test, and maintain production-grade Python applications and backend services. \n

• Write clean, modular, maintainable, and efficient Python code using appropriate design patterns and engineering best practices. \n

• Work with asynchronous programming, concurrency, multithreading or multiprocessing, and efficient resource management where appropriate. \n

• Diagnose complex application issues, investigate memory and performance bottlenecks, and improve application reliability. \n

• Build robust error handling, retry mechanisms, timeout management, and recovery strategies for failure-prone systems. \n

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Production Deployment & Operational Ownership

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• Own software from development through deployment, production monitoring, troubleshooting, and ongoing maintenance. \n

• Investigate and resolve production incidents involving application failures, latency, resource exhaustion, dependency failures, and unexpected behavior. \n

• Use logs, metrics, distributed tracing, profilers, and debugging tools to identify root causes and implement lasting fixes. \n

• Design and implement effective unit, integration, and regression testing strategies. \n

• Participate in on-call support, incident reviews, root-cause analysis, and preventive engineering improvements. \n

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Azure Cloud Engineering

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• Deploy, operate, and maintain Python applications and services hosted on Microsoft Azure. \n

• Work with Azure Functions, App Service, Azure Container Apps, Azure Kubernetes Service (AKS), or other relevant Azure hosting services. \n

• Understand cloud application lifecycle management, scaling, configuration, identity, networking, secrets, and dependency management. \n

• Troubleshoot production issues involving cold starts, resource limits, service availability, timeouts, throttling, and distributed failures. \n

• Implement reliable CI/CD workflows, deployment validation, monitoring, and rollback strategies. \n

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Backend & Distributed Systems

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• Design reliable APIs and backend services that integrate with databases, external services, and cloud-native components. \n

• Apply practical knowledge of HTTP, REST APIs, authentication, caching, connection pooling, and service-to-service communication. \n

• Build resilient applications using queues, asynchronous processing, event-driven communication, and distributed systems patterns where required. \n

• Understand consistency, idempotency, retries, race conditions, concurrency limits, and failure isolation. \n

• Collaborate with engineers across application, platform, infrastructure, and AI teams to deliver production-ready solutions. \n

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AI Application Engineering - Preferred

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• Integrate LLMs and AI capabilities into production applications using APIs and frameworks such as OpenAI, Azure OpenAI, LangChain, or Semantic Kernel. \n

• Build application-level AI features, including tool calling, retrieval-augmented generation (RAG), and workflow orchestration. \n

• Address practical production concerns such as latency, reliability, token usage, cost control, evaluation, and graceful failure. \n

• Apply conventional software engineering practices to AI applications, including testing, observability, security, and release management. \n

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\n, This position is primarily a software engineering role, not a data engineering or research role.

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• Not a data scientist or machine learning research position. \n

• Not primarily an ETL, data ingestion, or data pipeline development role. \n

• Not a role focused exclusively on notebooks, model experimentation, or AI proofs of concept. \n

• Not a position where cloud experience is limited to certifications, tutorials, or personal lab projects. \n

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We value engineers who can explain how their software behaves in production, how they investigate failures, and how they make systems more reliable over time.

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Interview & Work Logistics