Senior Manager - AI Engineering and Data Solutions

Data Engineer

Project Role : Data Engineer
Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.
Must have skills : AI Agents & Workflow Integration
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
The Manager of AI Engineering will lead the design, development, and deployment of scalable AI and machine learning solutions that drive innovation and performance across Vertex s enterprise. This role is central to transforming complex business challenges into practical, efficient, and secure AI systems. The individual will collaborate cross-functionally to deliver intelligent solutions that integrate seamlessly into Vertex s operational and product landscape.

Roles & Responsibilities:
AI Solution Engineering: Collaborate with stakeholders to design and implement AI-powered systems for new rollouts, enhancements, and maintenance of existing applications, ensuring alignment with enterprise standards.
System Specification & Oversight: Develop system specifications, test plans, and manage project scope, issue resolution, and delivery timelines.
Model Development & Deployment: Build and deploy machine learning and deep learning models using cloud-native AI services, ensuring scalability, reliability, and performance.
Advanced Analytics: Apply machine learning and AI techniques to extract insights from complex datasets and solve high-impact business problems.
Conversational AI: Lead the development of cloud-based Conversational AI applications, orchestrating multiple AI/ML services into cohesive solutions.
Cross-Functional Collaboration: Partner with data scientists, engineers, and business leaders to translate experimental models into production-ready systems.
Governance & Compliance: Ensure AI systems adhere to Responsible AI principles, including transparency, security, and ethical standards.
Continuous Improvement: Monitor model performance, implement feedback loops, and drive iterative enhancements across AI solutions.


Professional & Technical Skills:
Minimum 6 years of experience in Data Analysis, Statistics, Machine Learning, or Computer Science.
Minimum 5 years of hands-on experience in ML or deep learning engineering, including NLP/NLG applications.
Minimum 5 years of experience with AI/ML services from at least one major cloud provider (Azure, AWS, GCP).
Minimum 1 year of experience building Conversational AI applications and orchestrating AI/ML services.
Strong programming skills in Python and familiarity with cloud-native AI tools and frameworks.
Proven ability to deploy models in production environments with performance optimization and reliability.
Experience with enterprise data platforms, APIs, and integration patterns.
Strong communication and leadership skills with a track record of cross-functional collaboration.

Education & Trainings:
Bachelor s degree in Computer Science, Engineering, or related technical field.
Equivalent combination of education and experience may be considered (minimum 12 years total experience if no degree minimum 6 years with Associate s degree).


Additional Information:
Communicate with Clarity - Be clear, concise and actionable. Be relentlessly constructive. Seek and provide meaningful feedback.
Act with Urgency - Adopt an agile mentality - frequent iterations, improved speed, resilience. 80/20 rule – better is the enemy of done. Don t spend hours when minutes are enough.
Work with Purpose - Exhibit a We Can mindset. Results outweigh effort. Everyone understands how their role contributes. Set aside personal objectives for team results.
Drive to Decision - Cut the swirl with defined deadlines and decision points. Be clear on individual accountability and decision authority. Guided by a commitment to and accountability for customer outcomes.
Own the Outcome - Defined milestones, commitments and intended results. Assess your work in context, if you re unsure, ask. Demonstrate unwavering support for decisions.