Key Responsibilities :

Leadership & Strategy :

- Define and execute the enterprise Data and AI Engineering roadmap aligned with business objectives.

- Lead and mentor Data Engineering, AI Engineering, MLOps, and Platform Engineering teams.

- Build a culture of innovation, collaboration, engineering excellence, and continuous learning.

- Partner with executive leadership to identify opportunities for AI-driven business transformation.

- Establish engineering standards, governance, and best practices across data and AI initiatives.

Data Engineering :

- Design and oversee scalable cloud-based data platforms and modern data architectures.

- Lead implementation of data lakes, data warehouses, lakehouses, and real-time streaming platforms.

- Ensure high standards for data quality, security, governance, and compliance.

- Drive automation of data ingestion, transformation, orchestration, and monitoring.

- Optimize platform performance, reliability, scalability, and cost efficiency.

AI & Machine Learning Engineering :

- Lead engineering efforts for Machine Learning and Generative AI solutions.

- Build scalable AI platforms supporting model development, deployment, monitoring, and lifecycle management.

- Establish MLOps and LLMOps practices for production AI systems.

- Collaborate with Data Scientists and Product teams to operationalize AI use cases.

- Evaluate and implement emerging AI technologies, frameworks, and cloud-native services.

Architecture & Technology :

- Define enterprise architecture standards for data and AI platforms.

- Drive cloud modernization initiatives across AWS, Azure, or Google Cloud Platform.

- Promote API-first, event-driven, and microservices-based architectures.

- Ensure security, privacy, and responsible AI practices are embedded into engineering processes.

Delivery & Stakeholder Management :

- Lead large-scale digital transformation and modernization programs.

- Manage engineering budgets, resource planning, and vendor relationships.

- Collaborate with Product, Business, Security, Infrastructure, and Enterprise Architecture teams.

- Communicate technical strategies and program status to senior executives and stakeholders.

Operational Excellence :

- Establish engineering KPIs, SLAs, and operational metrics.

- Drive CI/CD, Infrastructure as Code, DevSecOps, DataOps, and MLOps adoption.

- Implement observability, monitoring, incident management, and platform reliability practices.

- Continuously improve engineering productivity and software delivery performance.

Required Qualifications :

- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field.

- Master's degree preferred.

- 15+ years of experience in software engineering, data engineering, or platform engineering.

- 7+ years of leadership experience managing enterprise engineering teams.

- Proven experience delivering enterprise-scale cloud data platforms and AI solutions.

- Experience leading geographically distributed engineering teams.

Technical Skills :

Data Platforms :

- Data Lakehouse architectures, Data Warehousing, Data Mesh, Data Fabric, Real-time Streaming, ETL/ELT Pipelines

Cloud Platforms :

- Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP)

Data Technologies :

- Databricks, Snowflake, Apache Spark, Delta Lake, Kafka, Airflow, dbt

Programming :

- Python, SQL, Scala, Java

AI & Machine Learning :

- Machine Learning platforms, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Vector Databases, Prompt Engineering, Model Evaluation, AI Governance

MLOps / DevOps :

- MLflow, Kubernetes, Docker, Terraform, GitHub Actions, Azure DevOps, Jenkins, CI/CD

Data Governance :

- Data Quality, Metadata Management, Data Catalog, Master Data Management, Data Security, Privacy Regulations, Responsible AI