Head - Data & AI/ML Engineering

Description :

- Design and lead the companys 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.

Data Engineering :

- Build and manage large-scale data pipelines and ETL/ELT systems.

- Implement modern architectures such as :

i. Data Lake

ii. Data Warehouse

iii. Lakehouse architectures

- Ensure scalability, reliability, and performance of data infrastructure.

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.

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 :

i. LLM integration

ii. Retrieval-Augmented Generation (RAG)

iii. Vector search systems

iv. AI agents and copilots

- Evaluate and integrate modern GenAI frameworks and tooling.

MLOps & AI Infrastructure :

- Build and maintain infrastructure for :

i. Model training

ii. Model versioning

iii. Model deployment

iv. Monitoring and observability

v. Experimentation frameworks

- Establish MLOps best practices for reliable production ML systems.

Data Governance & Quality :

- Implement frameworks for :

i. Data lineage

ii. Data quality monitoring

iii. Access controls

iv. Compliance and governance

AI Adoption Across Products :

- Partner with product engineering teams to enable:

i. Predictive analytics

ii. Recommendation systems

iii. Intelligent automation

iv. AI-driven decision systems

v. 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.

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 :

i. Large Language Models (LLMs)

ii. Retrieval-Augmented Generation (RAG)

iii. Vector databases and embedding systems

iv. AI agents and copilots

v. 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.