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Intermediate AI Software Developer

Procom -
Richmond, CA

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Job Details

Contract
$75 - $100 an hour

Qualifications

AI models
Software engineering
Data storage
C#
Application deployment
AI platforms (beyond public GPTs)
Computational framework
Model deployment
Agile software development
Cloud solution engineering
DevOps automation
Machine learning libraries
Cross-functional collaboration
Machine learning frameworks
Python
Design (software development lifecycle)
Cross-functional communication

Full Job Description

Intermediate AI Software Developer

Responsibilities and Deliverables:

Design, build, and test application components using C#, ASP.NET, .NET 10, and Azure, contributing to production deployments with monitoring, logging, and observability.
Utilize the AI development toolchain as configured by the team, including IDE integration (e.g., VS Code, Visual Studio), AI coding assistants (e.g., GitHub Copilot, Cursor), and agentic development tools (e.g., Copilot Agent Mode) to accelerate development workflows.
Implement components using agentic AI frameworks and orchestration patterns (e.g., Semantic Kernel, AutoGen, CrewAI, LangGraph, LangChain, LlamaIndex) as directed by the solution architecture.
Integrate LLM and AI capabilities into application features using Azure OpenAI, OpenAI APIs, and open-source models, iterating from prototype to production-ready implementation.
Build and maintain RAG pipeline components (embeddings, retrieval, re-ranking) and work with vector storage solutions (e.g., Azure AI Search, Cosmos DB, pgvector, Qdrant).
Implement prompt templates, memory management, and task chaining using frameworks such as PromptFlow, following established versioning and evaluation practices.
Execute AI evaluation and quality assurance tasks using LLM eval frameworks (e.g., Azure AI Evaluation SDK, DeepEval), including regression suites, safety testing, and quality gates for AI-generated outputs.
Contribute to AI observability and tracing implementation using Azure Monitor, Application Insights, LangSmith, MLflow Tracing (OpenTelemetry), and Weights & Biases.
Contribute to the development and refinement of the AI-Native SDLC Playbook, documenting implementation details, technical decisions, and lessons learned.
Follow the defined release and deployment process, ensuring AI-assisted development activities comply with enterprise guardrails, including change management, approvals, automated test gates, and deployment controls.
Apply application quality and release-readiness controls within the pipeline, including static analysis, dependency scanning, secret detection, code quality checks, and review gates for AI-generated code.
Contribute to measurable SDLC comparisons by tracking and reporting on delivery velocity, defect density, and automation coverage metrics.
Participate in knowledge-sharing through code reviews, pairing sessions, and documentation of reusable patterns and components.

Mandatory Requirements:

Undergraduate degree in Computer Science or a related STEM field.
5+ years of software development experience, including recent work with LLMs or AI integration.
Proficiency in Python and experience with AI/ML frameworks (e.g., OpenAI SDKs, LangChain).
Experience in C#, .NET Core, and object-oriented design. Experience deploying solutions to the cloud (Azure preferred).
Experience in agent-based design concepts and tools (AutoGen, Semantic Kernel, etc.).
Familiarity with RAG, GraphRAG, embeddings, and vector databases such as Cosmos DB, pgvector, or Qdrant.
Knowledge of APIs, CI/CD pipelines, Git, and Agile software development practices.
Ability to synthesize complexity and communicate AI capabilities clearly to diverse audiences.
Ability to collaborate and communicate effectively within cross-functional teams.

Preferred Requirements:

Experience with agentic frameworks and multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen Studio, Semantic Kernel Agents).
Hands-on use of AI coding assistants and agentic development tools (e.g., GitHub Copilot, Cursor) in project delivery.
Experience with AI evaluation, prompt management and versioning tools (e.g., Azure AI Evaluation SDK, DeepEval, PromptFlow).
Familiarity with AI observability and tracing tools (e.g., LangSmith, MLflow Tracing).
Experience with ASP.NET and .NET 8/10 for cloud-native web applications and APIs.
Azure platform experience including Azure App Service, Azure Functions, Container Apps, Azure SQL, Azure Key Vault, and Azure DevOps.
Experience with Infrastructure as Code (Terraform).

Intermediate AI Software Developer Assignment Length

12 months

Intermediate AI Software Developer Assignment Location

Richmond, BC-3 days in office

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