Sr Python / AI Backend Engineer (Not ML or Data Only)
Role Overview
Algoworks is looking for a Sr Python / AI Backend Engineer with strong hands-on experience building production-grade backend applications using Python, combined with practical experience developing AI-powered enterprise solutions beyond basic API integrations.
The ideal candidate is a strong backend software engineer who understands application architecture, APIs, distributed systems, databases, and cloud-native development, while also having hands-on experience implementing AI capabilities such as LLM applications, RAG, agents, embeddings, document intelligence, AI workflows, evaluation, and model-driven automation.
Strong knowledge of the Microsoft Azure ecosystem is required, including Azure AI Foundry, Azure OpenAI, Azure Data Factory, Azure AI Search, and related Azure services.
Experience with .NET / C# is a strong plus, particularly for candidates who have worked in enterprise environments where Python-based AI services need to coexist and integrate with existing .NET platforms.
This is not primarily a Data Engineering, Data Science, or traditional Machine Learning role.
Key Responsibilities
Backend Engineering
Design and develop scalable backend applications and services using Python.
Build production-grade REST APIs, microservices, asynchronous services, and event-driven applications.
Design backend components supporting AI-enabled workflows and enterprise applications.
Develop integrations with databases, queues, enterprise systems, APIs, and cloud services.
Implement secure, scalable, observable, and maintainable production services.
Participate in architecture, API design, code reviews, automated testing, and engineering standards.
Applied AI Engineering
Build real-world AI capabilities rather than simply wrapping third-party AI APIs.
Develop applications using LLMs, RAG, embeddings, vector search, agents, tool calling, and AI workflow orchestration.
Build AI services that process structured and unstructured enterprise information.
Implement document understanding, classification, extraction, summarization, reasoning, and automation workflows.
Design retrieval pipelines including chunking, indexing, embeddings, metadata filtering, reranking, and grounding.
Develop AI agents capable of interacting with APIs, enterprise applications, databases, and business workflows.
Implement prompt management, model selection, context management, guardrails, and structured outputs.
Build evaluation frameworks to measure AI solution quality, accuracy, hallucination, latency, and cost.
Improve AI application performance through caching, model routing, retrieval optimization, and related techniques.
Azure & AI Platform Engineering
Design and implement AI solutions using Microsoft Azure.
Build and deploy AI applications using Azure AI Foundry.
Work with Azure OpenAI Service and other Azure AI services.
Design enterprise search and RAG solutions using Azure AI Search.
Build and integrate data ingestion and orchestration pipelines using Azure Data Factory (ADF).
Integrate Azure data services with Python-based backend and AI applications.
Understand Azure identity, networking, security, storage, monitoring, and application hosting concepts.
Deploy applications using Azure services such as Azure App Service, Azure Functions, Azure Container Apps, AKS, or similar platforms.
Implement logging, monitoring, tracing, and operational controls using Azure-native capabilities.
Enterprise Application Integration
Integrate AI services into existing enterprise applications and backend platforms.
Expose existing business functionality securely to AI-powered applications and agents.
Design interfaces between Python AI services and existing enterprise systems.
Integrate Python-based AI services with .NET/C# applications where required.
Support modernization initiatives where AI capabilities are introduced into existing enterprise platforms.
Cloud & Production Engineering
Build containerized applications using Docker and modern CI/CD practices.
Deploy scalable backend and AI workloads into Azure.
Implement monitoring, logging, tracing, resiliency, security, and operational controls.
Work closely with DevOps and platform engineering teams to productionize AI workloads.
Required Skills
5+ years of software engineering / backend development experience.
Strong hands-on expertise with Python.
Strong experience with Python backend frameworks such as:
Strong understanding of:
Strong SQL and relational database fundamentals.
Practical experience building production AI / Generative AI applications.
Hands-on experience with multiple areas including:
Hands-on experience with Microsoft Azure.
Experience with Azure AI Foundry.
Experience with Azure OpenAI Service.
Experience with Azure Data Factory (ADF).
Experience with Azure AI Search or comparable enterprise search/vector search technologies.
Understanding of Azure application hosting, security, identity, networking, and monitoring.
Strong software engineering fundamentals including OOP, design patterns, testing, source control, and CI/CD.
Strongly Preferred
Hands-on development experience with C# and .NET / ASP.NET Core.
Experience working on enterprise platforms containing both Python and .NET services.
Strong Azure architecture experience.
Experience with:
Azure Functions
Azure Container Apps
Azure App Service
AKS
Azure Service Bus
Azure Storage
Key Vault
Application Insights
Experience with agent and AI orchestration technologies such as:
Microsoft Semantic Kernel
Azure AI Foundry Agent Service
LangChain
LangGraph
AutoGen
Similar agentic frameworks
Experience with vector and search technologies such as:
Experience with Docker, Kubernetes, GitHub Actions, or Azure DevOps.
Experience with event-driven technologies such as Kafka, RabbitMQ, Azure Service Bus, or similar platforms.
What We Are Specifically Looking For
The ideal candidate is a backend software engineer first, with strong applied AI and Azure engineering capability.
We are particularly interested in candidates who have:
Built substantial backend systems in Python, not primarily notebooks or data pipelines.
Written production application code rather than focusing predominantly on analytics or experimentation.
Implemented actual AI logic and AI workflows rather than only calling an LLM API.
Built APIs, services, agents, RAG systems, document intelligence solutions, or AI automation capabilities deployed into production.
Built AI solutions using Azure AI Foundry and Azure OpenAI.
Used Azure Data Factory to orchestrate data movement and ingestion where needed as part of enterprise solutions.
Worked with Azure-native services to build secure, scalable, production-grade applications.
Worked on enterprise-grade software requiring scalability, security, reliability, and maintainability.
Experience with .NET/C# is a significant advantage.
Candidates Who May Not Be the Best Fit
This role is not targeted primarily toward candidates whose experience is predominantly:
Data Engineering / ETL
Azure Data Factory development without backend software engineering
Data Warehousing
Spark / Databricks pipeline development
BI / Analytics
Data Science and statistical modeling
Traditional ML model training without significant software engineering
MLOps without hands-on application development
AI API integration without deeper AI application engineering
Notebook-based experimentation without production backend development
Preferred Technology Profile
Primary
Strong Plus
Ideal Candidate Profile
Senior Python Backend Engineer + Applied AI Engineer + Azure Engineer
The candidate should be capable of independently taking an AI use case through:
Business Requirement → Backend Architecture → Azure Architecture → AI Design → Python Implementation → Data Integration → Enterprise Integration → AI Evaluation → Production Deployment
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