Analytical Intelligence
Website:
ai-inter.com
Job details:
Job description
We are seeking a highly skilled Azure AI / ML Engineer to design, develop, and deploy advanced AI solutions on Microsoft Azure or AWS.
In this role, you will work closely with other AI engineers, software developers, and product stakeholders to build scalable, secure, and high-performance AI systems that drive business outcomes. Success in this position looks like performing rapid proof-of-concepts, moving successful outcomes to an MVP, then delivering production-ready AI solutions that improve decision-making, automate complex tasks, and generate measurable impact for the organization.
This role is critical to advancing our AI strategy and integrating cutting-edge technology into our products and services.
Responsibilities
- Design, develop, and deploy custom solutions using the best AI model & tools for the situation, These could be machine learning models using Azure AI Foundry, Azure ML, OpenAI API, Gemini and/or other related services
- Help support and take ownership of our AI product platform
- Collaborate with product teams to translate business requirements into solutions that deliver clear value
- Implement document ingestion, chunking, embeddings, indexing, metadata, and retrieval workflows.
- Build AI agents and tool-calling workflows using modern orchestration frameworks.
- Integrate AI applications with enterprise APIs, databases, document repositories, and SaaS platforms.
- Develop applications using OpenAI, Anthropic Claude, Gemini, and other LLM platforms when appropriate.
- Implement vector search, hybrid search, semantic search, and reranking techniques.
- Develop AI workflows using frameworks such as LangGraph, LangChain, Microsoft Agent Framework, Semantic Kernel, LlamaIndex, or equivalent technologies.
- Implement MCP or Plug-in-based integrations and tool interfaces where appropriate.
- Support document versioning, source change detection, lineage, provenance, and downstream impact analysis.
- Build evaluation processes to measure retrieval quality, groundedness, hallucination, tool-call accuracy, and overall application performance.
- Optimize AI applications for accuracy, latency, cost, and token usage.
- Implement logging, tracing, monitoring, retries, caching, and error handling for production AI systems.
- Apply secure AI engineering practices, including protection against prompt injection, data leakage, unauthorized tool usage, and insecure access to enterprise information.
- Build and maintain APIs and backend services supporting AI applications.
- Participate in technical design discussions and contribute to solution architecture.
- Follow software engineering, testing, Git, CI/CD, Azure DevOps, and GitHub best practices.
- Troubleshoot development and production issues across AI and application components.
- Create technical documentation for implemented solutions.
- Stay current with developments in LLMs, AI agents, RAG, MCP, model platforms, and Azure AI technologies.
Qualifications
Skills:
- Strong programming skills in Python.
- Experience developing production APIs and backend services.
- Experience with C# / .NET is a strong plus.
- Hands-on experience with Azure AI technologies, preferably including: Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning
- Experience using LLM APIs such as:, OpenAI, Anthropic Claude, Gemini,
- Strong understanding of:, Large Language Models, Retrieval-Augmented Generation, Embeddings, Vector databases, AI Agents, Tool / Function Calling,Prompt and context engineering
- Experience with one or more orchestration frameworks such as: LangGraph, LangChain, Microsoft Agent Framework, Semantic Kernel, LlamaIndex
- Understanding of MCP and modern AI tool integration patterns.
- Experience with vector search, semantic search, metadata filtering, hybrid retrieval, or reranking.
- Understanding of document processing, parsing, chunking, indexing, and retrieval pipelines.
- Familiarity with AI evaluation techniques such as: Groundedness, Retrieval relevance, Hallucination detection, Golden datasets, LLM-as-a-judge, Regression testing
- Understanding of GenAI security concerns such as: Prompt injection, Indirect prompt injection, Data leakage, Tool misuse, Authorization and access control
- Familiarity with production AI engineering practices including: Logging and tracing, Model and prompt versioning, Error handling, Rate limits, Caching, Cost and token monitoring
- Solid understanding of machine learning fundamentals, model evaluation, and statistical concepts.
- Familiarity with model fine-tuning, LoRA/QLoRA, PEFT, or related techniques is a plus.
- Experience with Azure services such as Azure Functions, App Service, Container Apps, Storage, Cosmos DB, SQL, Service Bus, or related services is desirable.
- Experience with REST APIs, asynchronous processing, and event-driven systems.
- Experience with Git, GitHub, Azure DevOps, CI/CD, and automated testing.
- Education:, Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field
Experience:
- 3+ years of experience developing and deploying AI / ML solutions, with at least 2 years on Azure
- Experience working in agile teams and collaborating cross-functionally
- Sorry we're unable to hire students or recent graduates
Certifications (preferred but not required):
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure Data Scientist Associate
Industry
- IT Services and IT Consulting
Employment Type
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