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:

  • FastAPI

  • Flask

  • Django

• Strong understanding of:

  • REST APIs

  • Microservices

  • Distributed systems

  • Authentication and authorization

  • Database integration

  • Asynchronous processing

  • Messaging and queues

  • Error handling and resiliency

• Strong SQL and relational database fundamentals.

• Practical experience building production AI / Generative AI applications.

• Hands-on experience with multiple areas including:

  • Large Language Models

  • RAG

  • Vector databases

  • Embeddings

  • AI agents

  • Function/tool calling

  • Prompt engineering

  • Document intelligence

  • AI workflow orchestration

  • AI evaluation and testing

• 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:

  • Azure AI Search

  • Pinecone

  • Qdrant

  • Weaviate

  • pgvector

  • Elasticsearch / OpenSearch

• 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

• Python

• FastAPI / Flask / Django

• REST APIs / Microservices

• Microsoft Azure

• Azure AI Foundry

• Azure OpenAI

• Azure AI Search

• Azure Data Factory

• SQL

• LLM / Generative AI

• RAG

• AI Agents

• Vector Search

• Docker / Cloud-native development

Strong Plus

• C#

• .NET / ASP.NET Core

• Semantic Kernel

• Azure Functions

• Azure Container Apps / AKS

• Azure Service Bus

• Kubernetes

• Event-driven architectures

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