job summary
This requirements profile is suitable for a Senior Generative AI Developer, AI Engineer, or Full-Stack Generative AI Developer role supporting enterprise Copilot, hospital intelligence, finance AI, operational analytics, or broader AI transformation initiatives.
Technical Skills
Programming & Software Engineering
o Python: Strong proficiency for application development, API integrations, and AI workflow implementation.
o Experience building REST APIs, microservices, and backend services.
o Strong understanding of software engineering best practices, version control with Git, automated testing, and CI/CD.
LLM APIs & AI Development
o Hands-on experience working with LLM providers and model platforms such as Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini, and Hugging Face/open-source models including Llama, Mistral, or Phi.
o Experience implementing chat applications, AI copilots, agents, function calling, tool integration, and multimodal AI capabilities.
RAG & AI Orchestration Frameworks
o Experience building Retrieval-Augmented Generation (RAG) solutions.
o Proficiency with orchestration frameworks such as LangChain, LangGraph, LlamaIndex, and Semantic Kernel.
o Experience creating agentic workflows and multi-agent systems.
Vector Databases & Embeddings
o Experience with embeddings, semantic search, and enterprise knowledge retrieval.
o Knowledge of vector database platforms such as Pinecone, Weaviate, Chroma, Azure AI Search, and FAISS.
o Understanding of chunking strategies, indexing, retrieval optimization, and relevance tuning.
Cloud Platforms & Infrastructure
Preferred cloud platform: Microsoft Azure.
Experience deploying AI applications using Azure OpenAI, Azure AI Services, Azure AI Search, Azure Kubernetes Service (AKS), Azure Functions, and Azure Storage.
Hands-on experience with Docker, Kubernetes, Infrastructure as Code, CI/CD pipelines, monitoring, and logging frameworks.
Data & Enterprise Integration
Experience integrating enterprise data sources, APIs, databases, and document repositories.
Knowledge of data processing, ETL patterns, authentication, authorization, and enterprise security controls.
Professional Competencies
Prompt Engineering: Design effective system prompts, instructions, structured output templates, and evaluation-driven prompt improvements for accuracy, consistency, and cost efficiency.
AI Safety, Governance & Responsible AI: Implement AI guardrails, content safety controls, privacy practices, responsible AI workflows, human-in-the-loop review patterns, and hallucination mitigation through grounding and validation.
System Evaluation & Monitoring: Establish evaluation frameworks to measure accuracy, relevance, latency, cost, hallucination rates, and user satisfaction. Monitor production systems and continuously optimize performance.
Communication & Collaboration: Translate complex AI concepts into business-focused solutions and collaborate effectively with product managers, data scientists, cloud engineers, security teams, and stakeholders.
location: Telecommute
job type: Contract
salary: $60 - 80 per hour
work hours: 8am to 5pm
education: Bachelors
responsibilities
Technical Skills
Programming & Software Engineering
o Python: Strong proficiency for application development, API integrations, and AI workflow implementation.
o Experience building REST APIs, microservices, and backend services.
o Strong understanding of software engineering best practices, version control with Git, automated testing, and CI/CD.
LLM APIs & AI Development
o Hands-on experience working with LLM providers and model platforms such as Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini, and Hugging Face/open-source models including Llama, Mistral, or Phi.
o Experience implementing chat applications, AI copilots, agents, function calling, tool integration, and multimodal AI capabilities.
RAG & AI Orchestration Frameworks
o Experience building Retrieval-Augmented Generation (RAG) solutions.
o Proficiency with orchestration frameworks such as LangChain, LangGraph, LlamaIndex, and Semantic Kernel.
o Experience creating agentic workflows and multi-agent systems.
Vector Databases & Embeddings
o Experience with embeddings, semantic search, and enterprise knowledge retrieval.
o Knowledge of vector database platforms such as Pinecone, Weaviate, Chroma, Azure AI Search, and FAISS.
o Understanding of chunking strategies, indexing, retrieval optimization, and relevance tuning.
Cloud Platforms & Infrastructure
Preferred cloud platform: Microsoft Azure.
Experience deploying AI applications using Azure OpenAI, Azure AI Services, Azure AI Search, Azure Kubernetes Service (AKS), Azure Functions, and Azure Storage.
Hands-on experience with Docker, Kubernetes, Infrastructure as Code, CI/CD pipelines, monitoring, and logging frameworks.
Data & Enterprise Integration
Experience integrating enterprise data sources, APIs, databases, and document repositories.
Knowledge of data processing, ETL patterns, authentication, authorization, and enterprise security controls.
Professional Competencies
Prompt Engineering: Design effective system prompts, instructions, structured output templates, and evaluation-driven prompt improvements for accuracy, consistency, and cost efficiency.
AI Safety, Governance & Responsible AI: Implement AI guardrails, content safety controls, privacy practices, responsible AI workflows, human-in-the-loop review patterns, and hallucination mitigation through grounding and validation.
System Evaluation & Monitoring: Establish evaluation frameworks to measure accuracy, relevance, latency, cost, hallucination rates, and user satisfaction. Monitor production systems and continuously optimize performance.
Communication & Collaboration: Translate complex AI concepts into business-focused solutions and collaborate effectively with product managers, data scientists, cloud engineers, security teams, and stakeholders.
qualifications
Technology Stack
Language: Python
Cloud: Azure
AI Services: Azure OpenAI, Azure AI Foundry, Azure AI Search
Frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel
Vector Stores: Pinecone, Weaviate, Chroma, Azure AI Search, FAISS
Containers: Docker, Kubernetes, AKS
DevOps: GitHub Actions, Azure DevOps
Monitoring: Azure Monitor, Application Insights
Databases: MongoDB, PostgreSQL, SQL Server
skills
AI Services
Application Insights
AI
AKS
UI development
Containers: Docker
Generative AI
Knowledge graphs
Healthcare AI
Kubernetes
Cloud: Azure
Azure
Azure DevOps
SQL Server
Databases: MongoDB
multi-agent
PostgreSQL
Python
AI Search
Agentic AI
AI transformation
Responsible AI
finance
Logistics
MS Teams
Foundry
operational analytics
safety
Semantic