Agentic AI Lead

Agentic AI Lead

AI Product & Technology Lead Data Engineering & Validation

Total Exp-10+ yrs; Agentic AI-3+ yrs

Location: Hyderabad

Role Overview

We are looking for an innovative AI Product & Technology Leader to shape the next generation of our Data Validation and Quality platform. This role is at the intersection of AI, Data Engineering, and Product Strategy — responsible for designing, planning, and implementing AI-driven validation systems that bring automation, intelligence, and autonomy to ETL, BI, and Data Quality validation processes. You will lead the design of Agentic AI frameworks and autonomous validation agents that can test, monitor, and self-heal data pipelines using cutting-edge GenAI technologies like Azure AI Foundry, LangGraph, AWS Bedrock, and LLM orchestration frameworks.

Key Responsibilities

1. AI Strategy & Vision for Data Validation

• Define the AI vision and roadmap for automating ETL and BI validation using Agentic AI.
• Identify opportunities to embed Generative AI and LLMs to automate validation rule generation, transformation logic extraction, and test coverage analysis.
• Align AI strategy with product and customer goals, driving faster, more intelligent data validation outcomes.
• Continuously explore emerging frameworks (LangGraph, Bedrock, Azure AI Foundry) for autonomous validation orchestration.

2. AI-Powered Validation Design & Implementation

• Architect and lead development of AI-driven validation engines that analyze ETL, BI, and data pipelines in real time.
• Build Autonomous Validation Agents capable of:
  • Extracting transformation logic from ETL and SQL procedures.
  • Generating validation test cases automatically based on lineage and metadata.
  • Comparing data across layers (source, staging, target, BI dashboards) using GenAI reasoning.
  • Detecting anomalies, schema drift, and reconciliation issues automatically.
  • Performing self-healing and intelligent reruns of failed validations.
• Implement AI copilots that assist users in creating, interpreting, and debugging validation rules using natural language.

3. Data Quality & Observability Intelligence

• Enhance the platform’s Data Quality and Observability capabilities using AI-based anomaly detection, rule learning, and trend forecasting.
• Build proactive observability agents that detect data integrity, freshness, and accuracy issues before they impact reports.
• Integrate GenAI reasoning for intelligent root-cause analysis and contextual recommendations.
• Implement feedback loops where validation outcomes improve the AI’s future accuracy (learning validation behavior patterns).

4. Product Strategy & Leadership

• Partner with Product Management to define the AI-driven product roadmap and translate customer pain points into intelligent validation features.
• Work closely with Engineering, Data Science, and QA teams to design scalable, modular AI components.
• Transform traditional validation workflows into autonomous, AI-augmented DataOps processes.
• Establish AI impact metrics (accuracy, automation coverage, reduction in manual effort) to measure success.

5. AI Planning, Design, and Governance

• Define and drive AI system design reviews, architectural blueprints, and validation planning processes.
• Implement AI governance for validation reliability, explainability, and traceability.
• Collaborate with cloud and AI platform providers (Azure, AWS, etc.) for scalable AI infrastructure.
• Establish standards for LLM usage, data security, and ethical AI in automated validation scenarios.

Required Qualifications

-10+ years of experience in Data Engineering, AI-driven Data Platforms, or Product Engineering, with at least 3–4+ years focused on AI/ML, Generative AI, or Agentic AI architectures.

-Proven track record in applying AI to automate and enhance data validation, quality, and observability workflows.

-Deep expertise in LLMs, Autonomous AI agents, and GenAI frameworks such as LangChain, LangGraph, Azure AI Foundry, AWS Bedrock, or OpenAI APIs.

-Hands-on experience in designing AI-powered validation systems, including schema intelligence, anomaly detection, reconciliation, and auto-healing pipelines.

-Strong programming background in Python, SQL, and Spark, with knowledge of AI workflow orchestration tools (e.g., Airflow, dbt, Prefect).

-Solid understanding of vector databases, retrie…