Job Summary
To apply advanced data science techniques, build scalable models, and generate actionable insights that drive business performance, optimize processes, and support data-driven decision-making.
SR
Demand ID
CU L4* HR L4 : ERS CU-DE-Data (HDU)-Data Engineering
Designation Sr AI Engineer
Location: Location : PAN India (Prefer Noida or HYD)
Band & Sub Band Band : E3.1
Excalibur ID (New Project/Extension)* Experience : 8+ Years
Project Name & Code Project : BI_CSS_VolumeForecasting_FY26
No. of Positions: 1
Bill Start Date: BSD : 1st April 2026
Bill Rate Sell rate : /$35
Primary Skill Area: Primary Skill : Gen AI, Vector Database, LLM, ML, NLP
Secondary Skill : Python, LangChain/AutoGen, RAG, API integration, prompt engineering
Skillset/JD: JD: A Senior Azure AI Engineer is responsible for designing, developing, deploying, and optimizing AI/ML and generative AI solutions using Microsoft Azure’s cloud ecosystem.
Design, build, and maintain AI/ML applications using Azure Machine Learning, Azure AI Services, Azure AI Studio, and SDKs.
Develop and deploy machine learning models (Python‑based) using Azure ML pipelines.
Implement LLM/GenAI solutions using Azure AI Foundry, Azure OpenAI, Cognitive Services, and vector search.
Create AI-driven solutions in vision, language, speech, document intelligence, and knowledge mining.
Apply Responsible AI principles ensuring fairness, transparency, security, and compliance.
Work closely with data engineers, architects, analysts, and business stakeholders to interpret requirements and deliver solutions
Reporting/Owner SAP id: RM : 51948803
Key Responsibilities
2. Design, test, and refine algorithms for data processing, feature extraction, and pattern detection.
3. Perform exploratory data analysis, mining, and visualization to identify trends and business opportunities.
4. Collaborate with business and technology teams to translate data insights into strategic solutions.
5. Utilize deep learning, statistical modeling, and big data tools to enhance analytical capabilities.
6. Ensure data quality, governance, and best practices in model development and deployment.
7. Optimize and automate data workflows to improve efficiency and scalability.
8. Stay updated with emerging data science technologies and best practices to drive innovation.