招聘

Data Analytics Manager (AI Engineer - Generative & Agentic AI)
India, Karnataka, Bangalore
·
On-site
·
Full-time
·
1w ago
Overview
We’re looking for an AI Engineer to lead the design and deployment of Agentic AI solutions using the Microsoft Azure AI ecosystem. Your primary focus will be on building autonomous multi-agent systems that reason, act, and collaborate to solve complex tasks.
Generative AI (LLMs like GPT) will complement your work but is not the core. You'll drive AI-powered automation by integrating Azure AI tools with enterprise systems and workflows.
You’ll work closely with business, data engineering, architecture, and DevOps teams to deliver intelligent, scalable, and secure AI solutions.
Responsibilities
- Agentic AI Development:
Design and deploy autonomous agents using Azure AI Agent Service and related tools. Define agent logic, goals, and collaboration flows.
- End-to-End Ownership:
Lead solutioning from design to deployment and scaling of multi-agent systems.
- Cloud Integration:
Leverage Azure services like AI Studio,OpenAI,Azure ML,Functions, and Kubernetes for deployment and orchestration.
- Generative AI Use:
Embed LLMs (e.g., GPT) where needed for summarization, Q&A, retrieval-augmented generation, or natural language tasks.
- Enterprise Automation:
Extend agent capabilities using Power Platform tools like Power Automate,Power Apps, and Dataverse.
- Innovation & Reusability:
Contribute to internal R&D, reusable assets, and share learnings across teams.
Qualifications Required Qualifications
- 3–6 years in software engineering or AI/ML development, including 1–2 years with agent-based AI.
- Strong proficiency in Python for AI workflows, APIs, and automation.
- Experience deploying AI using Microsoft’s Azure AI stack (AI Studio, Azure ML, OpenAI, Agent Service).
- Understanding of agent orchestration, stateful logic, decision systems, and tool-using agents.
- Familiarity with prompt engineering,RAG, or LLM fine-tuning.
- Knowledge of cloud-native architecture, APIs, event-driven systems, and secure deployments.
Technical Skills
- Python: Core scripting for pipelines, agent logic, and orchestration.
- JavaScript/TypeScript: For AI-embedded apps (e.g., Power Apps extensions, web integrations).
- Power Fx, DAX, M: For Power Platform and Power BI customizations.
- API & Data Standards: JSON, YAML, REST, GraphQL, Microsoft Graph.
- C#/.NET (Preferred): Useful for Azure Functions and Bot Framework.
- Visualization (Nice to have): Custom Power BI visuals using D3.js or Chart.js.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
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关于Microsoft

Microsoft
PublicMicrosoft Corporation is an American multinational technology conglomerate headquartered in Redmond, Washington.
10,001+
员工数
Redmond
总部位置
$3000B
企业估值
评价
3.8
5条评价
工作生活平衡
4.1
薪酬
4.3
企业文化
3.4
职业发展
3.2
管理层
3.0
65%
推荐给朋友
优点
Excellent compensation and benefits package
Four-day workweek with improved work-life balance
Supportive managers and teams
缺点
High-pressure environment causing anxiety
Unprofessional interview processes
Limited creative work opportunities
薪资范围
5,620个数据点
Senior/L5
Senior/L5 · Account Management
5份报告
$209,483
年薪总额
基本工资
$181,941
股票
-
奖金
-
$194,895
$209,483
面试经验
1次面试
难度
4.0
/ 5
时长
14-28周
体验
正面 0%
中性 0%
负面 100%
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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