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 up-to-date 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 PipelinesCloud Platforms : - Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP)Data Technologies : - Databricks, Snowflake, Apache Spark, Delta Lake, Kafka, Airflow, dbtProgramming : - Python, SQL, Scala, JavaAI & Machine Learning : - Machine Learning platforms, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Vector Databases, Prompt Engineering, Model Evaluation, AI GovernanceMLOps / DevOps : - MLflow, Kubernetes, Docker, Terraform, GitHub Actions, Azure DevOps, Jenkins, CI/CDData Governance : - Data Quality, Metadata Management, Data Catalog, Master Data Management, Data Security, Privacy Regulations, Responsible AI (ref:hirist.tech)

📌 Director - Data & AI Engineering (Chennai)
🏢 Sampoorna Consultants
📍 Chennai