AI Developer

Evolver Commercial•Sf, CA

About The Position

The AI Developer is responsible for designing and delivering enterprise-grade AI, application, and agentic solutions that combine Python development, API engineering, enterprise data access, and generative AI capabilities. This role emphasizes hands-on development across Python-based AI applications, databases, APIs, and Azure services, with the ability to build governed, scalable AI solutions such as copilots, Retrieval-Augmented Generation workflows, and multi-agent systems.

Requirements

8 years of experience working as a developer in the .NET and Python technology stack, building enterprise applications, services, and integrations.
2 years of experience developing AI/ML solutions, including generative AI applications and agentic workflows.
1 years of hands-on experience building agentic AI applications.
2 years of experience with distributed systems, APIs, Azure cloud services, and production-grade application development using modern frameworks.
2 years of experience with Azure AI Foundry, Claude Code development, Copilot Studio, Azure ML, and related enterprise AI tooling.
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Nice To Haves

Strong expertise in application architecture, object-oriented design, database design, and enterprise system integration.
Strong proficiency in Python, SQL, and modern development toolsets, with experience building AI applications, automation workflows, APIs, and enterprise services.
Strong experience building REST APIs, Python services, and enterprise applications using Azure-native development patterns.
Experience with DevOps practices, including GitHub, Azure DevOps, and CI/CD pipelines.
Strong understanding of governance, security, compliance, and operational controls for enterprise AI solutions.
Ability to translate complex business requirements into scalable, reliable, and governed AI-enabled solutions.
Experience with vector databases such as Azure Search, Cosmosdb.
Familiarity with machine learning frameworks such as PyTorch, TensorFlow.
Experience integrating AI services into enterprise applications using Python, APIs, and cloud-native development patterns.
Experience with .NET, ASP.NET, C#, microservices, or backend service development in enterprise environments is a plus.
Background in building governed, production-grade AI solutions in regulated enterprise environments.
Azure AI Engineer Associate certification is strongly preferred.

Responsibilities

Design, build, customize, and deploy AI models, agents, copilots, and multi-agent systems for enterprise use.
Develop agentic workflows and implement prompt orchestration patterns for production-ready solutions using Python, Azure AI Foundry, Claude Code, and related enterprise AI platforms.
Design and develop Python-based enterprise applications, backend services, automation components, and reusable modules using modern software engineering practices.
Build and maintain scalable application services, integrations, and data access layers with a focus on performance, reliability, and observability.
Build and integrate Model Context Protocol (MCP) servers to allow agents to safely query databases and execute APIs.
Select and fine-tune Large Language Models (LLMs) via the Azure AI Foundry Model Catalog.
Implement end-to-end tracing, monitoring, and quality evaluations for operational safety.
Develop Python-driven Retrieval-Augmented Generation pipelines that integrate enterprise data sources, embeddings, and vector stores.
Perform prompt engineering, model tuning, evaluation, and monitoring to continuously improve solution quality.
Develop and expose enterprise services through APIs and application components deployed on Azure, integrating AI capabilities into business platforms and developer workflows.
Implement CI/CD pipelines and support the end-to-end lifecycle of AI and data solutions in production.
Ensure compliance with security, privacy, and regulatory standards through role-based access, logging, and audit controls.
Create and maintain documentation for solution architecture, data flows, operational procedures, and supportability.