HEAD - DATA & AI/ML ENGINEERING

Data Platform Architecture

• Design and lead the company's modern data platform architecture. • Build scalable systems for data ingestion, processing, transformation, and storage. • Enable reliable and governed data access for analytics, ML models, and AI applications.

2. Data Engineering

• Build and manage large-scale data pipelines and ETL/ELT systems. • Implement modern architectures such as: Data Lake Data Warehouse Lakehouse architectures • Ensure scalability, reliability, and performance of data infrastructure.

3. AI / Machine Learning Engineering

• Build infrastructure for training, deploying, and monitoring ML models. • Develop scalable ML pipelines and feature engineering systems. • Enable product teams to embed AI-powered capabilities into applications.

4. Generative AI & LLM Systems

• Drive adoption of Generative AI technologies across products. • Design systems using large language models (LLMs) for intelligent automation and data-driven applications. • Build architectures for: LLM integration Retrieval-Augmented Generation (RAG) Vector search systems AI agents and copilots • Evaluate and integrate modern GenAI frameworks and tooling.

5. MLOps & AI Infrastructure

• Build and maintain infrastructure for: Model training Model versioning Model deployment Monitoring and observability Experimentation frameworks • Establish MLOps best practices for reliable production ML systems.

6. Data Governance & Quality

• Implement frameworks for: Data lineage Data quality monitoring Access controls Compliance and governance

7. AI Adoption Across Products

• Partner with product engineering teams to enable: Predictive analytics Recommendation systems Intelligent automation AI-driven decision systems GenAI-powered product features

Leadership Responsibilities

• Build and lead the Data & AI/ML Engineering Pod. • Mentor data engineers, ML engineers, and AI engineers. • Define the technical roadmap for data and AI systems. • Establish best practices for data engineering, ML systems, and AI infrastructure. • Drive adoption of AI and GenAI capabilities across engineering teams.

(C) TECHNICAL KNOWLEDGE, SKILL-SET & QUALIFICATION

Data Platforms Strong experience in: • Data pipelines and distributed data processing • Data lake / lakehouse architectures • Streaming and real-time data processing • Large-scale analytics platforms

Machine Learning Systems Experience with: • ML pipelines and feature stores • Model training and deployment • ML model monitoring and lifecycle management

Generative AI Strong understanding of: • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • Vector databases and embedding systems • AI agents and copilots • Prompt engineering and LLM orchestration frameworks

Required Qualifications • Experience leading Data Engineering or AI/ML Engineering teams. • Strong background in large-scale data systems. • Experience building production machine learning systems. • Good understanding of Generative AI and LLM-based applications. • Experience designing scalable data and AI platforms. Preferred Qualifications • Experience building AI-powered enterprise platforms. • Experience integrating GenAI features into production systems. • Experience with large-scale data environments. • Familiarity with geospatial or location intelligence data. Leadership Expectations The Head of Data & AI/ML Engineering will: • Define the data and AI strategy for the company. • Build scalable data platforms and AI infrastructure. • Enable product teams to leverage data, ML, and GenAI capabilities. • Drive innovation through AI-powered product development.