AI Engineer

Client Name Microsoft Inc Job Location/Client Location (with City & State) Redmond, WA Remote ok (Yes / No) Yes Sell Rate $70 USD Job Title/Role AI Engineer JD Responsibilities • Architect Agentic AI solutions using Microsoft Foundry, Azure OpenAI, LangChain, LangGraph & multi-agent frameworks • Well-versed with the Microsoft Agentic Framework (MAF) • Build RAG pipelines, vector DB integrations & autonomous workflow orchestration • Design and lead ML project lifecycles - data prep, modeling, training, evaluation, deployment & MLOps • Govern full SDLC for Data, ML, and GenAI platforms • Drive RFP/RFI solutioning, technical proposals, estimations & client workshops • Ensure strong security, compliance, governance (GDPR, CCPA, PII) • Produce robust architecture blueprints, ML design docs, and runbooks • Engage with customer IT and business leaders to understand pain points, priorities, success measures, and risks. • Design secure, scalable data and AI solutions to deliver measurable business value. • Lead architecture design sessions, develop data/AI and analytics roadmaps to drive PoCs and MVPs. • Accelerate adoption and ensure long-term technical viability. Skill & Experience • 15+ years in Data/AI/ML Engineering or 15+ years • Strong exposure to Microsoft Azure stack including Synapse, Fabric, Foundry, Copilot Studio, Databricks, Power BI • Hands-on with: o ML projects (supervised/unsupervised, forecasting, NLP, deep learning) o ML modeling tools: Python, PySpark, Azure ML, Databricks, Scikit-learn o Microsoft Foundry, Microsoft Agentic Framework o LLMs, embeddings, vector databases, RAG/GraphRAG o CI/CD, MLOps/LLMOps, SDLC

Job Summary

We are looking for GenAI / Agentic Engineers across junior and senior levels to design, build, and deploy AI applications leveraging LLMs and agentic frameworks. You'll handle all phases of the SDLC and work with stakeholders to create reliable, scalable AI solutions. Senior engineers will lead design and customer interactions; junior engineers will focus on model training, fine-tuning, and data preparation.

Responsibilities

Core

• Develop AI solutions using frameworks such as Microsoft Agent Framework - Autogen, Semantic Kernel, Copilot Studio
• Implement data science / machine learning algorithms defined by Solution Architects.
• Follow all SDLC phases: requirements, design, development, testing, deployment, and maintenance.
• Collaborate with customers/stakeholders on project status and deliverables.

Key Responsibilities

We are looking for GenAI / Agentic Engineers across junior and senior levels to design, build, and deploy AI applications leveraging LLMs and agentic frameworks. You'll handle all phases of the SDLC and work with stakeholders to create reliable, scalable AI solutions. Senior engineers will lead design and customer interactions; junior engineers will focus on model training, fine-tuning, and data preparation.

Responsibilities

Core

• Develop AI solutions using frameworks such as Microsoft Agent Framework - Autogen, Semantic Kernel, Copilot Studio
• Implement data science / machine learning algorithms defined by Solution Architects.
• Follow all SDLC phases: requirements, design, development, testing, deployment, and maintenance.
• Collaborate with customers/stakeholders on project status and deliverables.

Skill Requirements

Required Skills & Qualifications

Core Technical Skills (All Levels)

• Hands-on working experience on Azure AI Foundry and Azure services/PAAS.
• Python (scripting, data manipulation); familiarity with NumPy, Pandas, Matplotlib, Scikit Learn etc..
• GenAI / Agent frameworks: Autogen, Semantic Kernel, Azure AI Foundry, Azure OpenAI,
• Understanding of machine learning fundamentals: supervised vs. unsupervised learning, neural networks, NLP.
• Familiarity with GenAI tools: MCP Server, Hugging Face Transformers, OpenAI APIs, and diffusion models (for image generation).
• Ability to work across the SDLC with clean code, testing, and documentation.

Other Requirements

Nice-to-Have

• Experience with AutoGen / LangGraph multi-agent patterns and orchestration.
• Knowledge of RAG pipelines, vector databases, prompt optimization, and safety/guardrails.
• Exposure to MLOps (model versioning, deployment, monitoring).
• Cloud certifications (Microsoft Azure) is a plus.

What You'll Deliver

• Production-ready GenAI/Agentic applications.
• Fine-tuned models and reproducible experiments.
• Clear documentation, test coverage, and deployment pipelines.
• Regular updates on project status and deliverables to stakeholders

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