Role : AI Engineer (AI CoE)

Reporting To : Head - AI CoE

Location : Any

Industry : Experience in Telecom & Cloud Services preferred.

Qualifications :

- Bachelor's/Master's in Computer Science, Data Engineering, or related field

- 5+ years in data engineering with exposure to AI/ML workflows

- Expertise in Python, SQL, Spark, ETL frameworks

- Experience with data modeling, warehousing (Snowflake, Redshift), and streaming (Kafka, Flink)

- Familiarity with cloud platforms (Azure, AWS, GCP) and big data ecosystems

- Knowledge of AI data prep (LLMs, embeddings, vector DBs) and ML Ops

- Strong problem-solving, communication, and stakeholder management skills

Job Summary :

Responsible for building scalable data pipelines and architectures to enable AI/ML solutions. Collaborates with Data Scientists and Business stakeholders to deliver high-quality, AI-ready datasets and optimize data workflows for Generative AI and LLM applications.

Key Responsibilities :

- Design and maintain scalable data pipelines for AI/ML models

- Develop and manage data ingestion, transformation, and storage solutions

- Optimize workflows for Generative AI and LLM applications

- Implement streaming data pipelines and ensure performance and cost efficiency

- Ensure data quality, governance, and compliance standards

- Collaborate with AI Engineers and Data Scientists for seamless integration

- Prepare datasets for AI/ML models including embeddings and RAG pipelines

- Document data architectures, processes, and best practices

- Build Proof of Concepts (POCs) within 6-8 weeks and demonstrate high accuracy (90%+)

- Monitor and maintain deployed models for accuracy, reliability, and scalability

- Document technical designs, use cases, and best practices

Objectives :

- Enable high-quality, AI-ready data pipelines for enterprise AI initiatives

- Accelerate time-to-market for AI solutions through efficient data engineering

- Ensure compliance with data governance and ethical AI standards

Key Result Areas :

- Timely delivery of AI-ready datasets for model development

- Reduction in data processing time and cost

- Data reliability and accuracy for AI/ML models

- Compliance with governance and security standards

- Reducing operational costs by at least 15-20%. Achieve 20% reduction in operational cost via AI-driven automation.

Expected Outcomes :

- Rapid development and deployment of AI solutions

- Improved decision-making and business performance through AI-driven insights

- Strong AI governance and minimized risk exposure

Key Competencies :

- Data Architecture & Pipeline Design. ETL Development & Data Integration

- Big Data Technologies (Spark, Hadoop), Cloud Data Platforms (Azure, GCP)

- Data Modeling & Warehousing. Performance Optimization & Scalability

- Data Governance & Quality Management

- Applied AI Engineering & Technical Curiosity

- AI-ready Data Preparation (LLMs, RAG, Vector DBs)

- ML Ops & Deployment Support

- Problem Solving & Business Value Orientation

- Cross-functional Collaboration

- Data Storytelling & Influential Communication

- Ethical AI Practices & Regulatory Awareness

- Product Thinking & Agile Delivery

- Stakeholder Management & Change Leadership

- Data-driven Decision Making