We are seeking a Senior Foundry AI Developer to design, build, test, and deploy enterprise grade AI solutions using Microsoft Foundry, formerly known as Azure AI Foundry, and associated Microsoft Azure services.
The role involves developing generative AI applications, AI agents, agentic workflows, retrieval augmented generation solutions, prompt and model orchestration, evaluation frameworks, and production grade integrations. The developer will work closely with data, engineering, architecture, security, risk, and business teams to operationalize AI use cases.
The successful candidate will combine strong Python and software engineering capabilities with practical experience in large language models, retrieval, AI agents, evaluation, observability, security, and responsible AI. Solutions should be scalable, secure, traceable, cost effective, and suitable for use in a regulated enterprise environment.
This role requires full coverage with an EST work schedule.
- Design, build, test, and deploy generative AI applications and agentic solutions using Microsoft Foundry and related Azure services.
- Translate business opportunities and operational problems into clearly defined AI use cases, technical designs, and implementation plans.
- Develop enterprise AI assistants, copilots, autonomous or human supervised agents, and multi step agentic workflows.
- Build retrieval augmented generation solutions using enterprise documents, databases, APIs, internal data, and approved external data sources.
- Design document ingestion, parsing, chunking, metadata, embedding, indexing, retrieval, reranking, grounding, and citation strategies.
- Implement vector, keyword, semantic, and hybrid search using Azure AI Search or equivalent technologies.
- Develop prompts, system instructions, reusable prompt templates, structured outputs, tool calling mechanisms, and model orchestration logic.
- Implement single agent and multi agent patterns, including workflow state, memory, session management, tool permissions, error handling, handoffs, and human approval points.
- Select and evaluate appropriate models from Microsoft Foundry based on accuracy, latency, context requirements, data sensitivity, cost, and operational constraints.
- Develop production quality Python services, REST APIs, event driven components, and integration layers.
- Integrate AI solutions with enterprise applications, data platforms, APIs, databases, document repositories, and business workflows.
- Implement secure access using Microsoft Entra ID, managed identities, role based access control, private networking, secret management, and least privilege principles.
- Establish safeguards covering content safety, prompt injection, data leakage, harmful output, personally identifiable information, access control, and inappropriate tool execution.
- Design automated and human evaluation frameworks covering relevance, groundedness, correctness, completeness, safety, bias, retrieval quality, task completion, latency, and cost.
- Create representative test datasets, golden datasets, evaluation rubrics, regression suites, and production quality acceptance criteria.
- Implement end to end tracing, logging, token and cost monitoring, performance dashboards, alerts, and production observability using Application Insights, Azure Monitor, or equivalent tools.
- Define and implement LLMOps and MLOps practices, including prompt and model versioning, source control, automated testing, CI/CD, deployment, rollback, and controlled experimentation.
- Optimize AI applications for response quality, reliability, throughput, latency, token consumption, and infrastructure cost.
- Investigate production incidents, retrieval failures, poor quality responses, model behavior issues, and integration failures.
- Produce technical documentation, solution designs, operating procedures, support guides, and architectural decision records.
- Conduct code reviews and mentor developers in generative AI, agentic engineering, responsible AI, and production software development practices.
- Collaborate with cybersecurity, privacy, legal, risk, and model governance teams to ensure enterprise and regulatory requirements are met.
- Explain AI capabilities, limitations, risks, and implementation trade offs clearly to technical and business stakeholders.
- 6+ years of experience in software engineering, AI engineering, machine learning, data science, or related technology roles.
- 3+ years of strong hands on Python development experience.
- Demonstrated experience building and deploying production generative AI or large language model applications.
- Hands on experience with Microsoft Foundry or Azure AI Foundry, Foundry models, Azure OpenAI, or closely related Azure AI services.
- Strong understanding of large language models, tokenization, context management, embeddings, prompting, structured output, tool calling, and model selection.
- Strong practical experience designing and implementing retrieval augmented generation solutions.
- Experience with vector databases or search platforms, preferably Azure AI Search.
- Experience developing AI agents and agentic workflows involving tools, state, memory, orchestration, approvals, and error recovery mechanisms.
- Strong Python engineering skills, including APIs, asynchronous programming, testing, exception handling, packaging, dependency management, and maintainable application design.
- Experience developing REST APIs and services using frameworks such as FastAPI, Flask, or equivalent technologies.
- Experience with Azure services such as Azure AI Search, Blob Storage, Cosmos DB, SQL Database, Key Vault, Microsoft Entra ID, Application Insights, Azure Monitor, Functions, Container Apps, or AKS.
- Understanding of SQL, NoSQL, document stores, vector search, and enterprise data integration patterns.
- Experience with Docker, Git, CI/CD, automated testing, environment configuration, and cloud deployment.
- Strong understanding of AI evaluation, observability, tracing, regression testing, model monitoring, and production support practices.
- Knowledge of responsible AI, content safety, security, privacy, model risk, data protection, and enterprise governance requirements.
- Ability to identify when generative AI is appropriate and when deterministic automation, search, analytics, or conventional software is a better solution.
- Strong analytical, troubleshooting, consulting, documentation, and communication skills.
- Ability to work directly with business stakeholders to challenge assumptions, clarify expected outcomes, and define measurable success criteria.
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or an equivalent discipline.
- Experience with Microsoft Agent Framework, Foundry Agent Service, Semantic Kernel, LangChain, LangGraph, or comparable orchestration frameworks.
- Experience developing multi agent workflows and human in the loop approval processes.
- Experience with Model Context Protocol based tools and enterprise tool integration.
- Experience with traditional machine learning, predictive analytics, NLP, document intelligence, or multimodal AI.
- Experience with model fine tuning, synthetic data generation, prompt optimization, or model distillation.
- Experience with infrastructure as code using Bicep, Terraform, or equivalent technologies.
- Experience implementing private endpoints, network isolation, API Management, managed identities, and secure cloud architecture.
- Knowledge of AI red teaming, adversarial testing, prompt injection testing, and automated safety evaluation.
- Experience working with investment management, asset management, fixed income, financial products, compliance, risk, or other regulated financial services functions.
- Familiarity with Microsoft Fabric, Power BI, Snowflake, Databricks, or enterprise analytics platforms.
- Relevant Microsoft Azure AI, data science, cloud development, or architecture certifications.