Gen AI Architect – Agentic AI for Data Engineering Platform Automation
Role Summary
We are seeking a Gen AI Architect to design and lead the implementation of agentic AI solutions that automate and modernize our data engineering platform. This role sits at the intersection of generative AI architecture and data engineering, requiring someone who can design intelligent, autonomous systems (agents) that orchestrate, monitor, and optimize data pipelines, quality checks, transformations, and operational workflows — with minimal human intervention.
Experience: 15+ years overall in technology/architecture roles, with demonstrated hands-on experience architecting agentic AI / LLM-based systems, and working knowledge of data engineering platforms and practices.
Key Responsibilities
Agentic AI Architecture & Strategy
- Design end-to-end architecture for multi-agent systems that automate data engineering tasks (pipeline orchestration, data quality remediation, schema drift detection, anomaly resolution, metadata management, etc.)
- Define agent design patterns: planning/reasoning loops, tool-use frameworks, memory/state management, multi-agent orchestration, human-in-the-loop checkpoints
- Select and evaluate frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom orchestration) suited to enterprise-scale deployment
- Establish architecture standards for LLM integration, prompt/context management, retrieval-augmented generation (RAG), and tool/function calling within data workflows
Data Engineering Platform Automation
- Partner with data engineering teams to identify high-value automation opportunities across ingestion, transformation, orchestration (Airflow/dbt/Spark, etc.), and observability layers
- Architect agents that can read pipeline metadata, logs, and lineage to autonomously detect, diagnose, and (where appropriate) remediate failures
- Ensure automation solutions integrate cleanly with existing data platforms (data warehouses/lakehouses, ETL/ELT tools, orchestration engines, catalogs)
Technical Leadership & Governance
- Define guardrails, evaluation frameworks, and monitoring for agent reliability, safety, and cost (token usage, latency, hallucination risk)
- Establish patterns for human oversight, escalation, and rollback in autonomous workflows
- Lead technical design reviews, POCs, and pilot-to-production transitions for agentic solutions
- Mentor engineering teams on agentic design principles and best practices
Required Qualifications
Experience
- 15+ years in software/data/AI architecture roles, including enterprise-scale system design
- 2+ years of hands-on experience designing or implementing agentic AI / LLM-based systems (not just prototypes — production or near-production exposure preferred)
- Solid foundational experience in data engineering: pipeline design, ETL/ELT, orchestration tools, data quality frameworks (does not need to be a hands-on data engineer, but must be conversant enough to architect solutions that integrate with these systems)
Technical Skills
- Strong understanding of LLM architectures, prompt engineering, RAG, embeddings/vector stores, and function/tool calling
- Experience with agent orchestration frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalents) or building custom orchestration layers
- Familiarity with data engineering tooling: Airflow, dbt, Spark, Kafka, cloud data warehouses/lakehouses (Snowflake, Databricks, BigQuery, Redshift, etc.)
- Cloud architecture experience (AWS, Azure, or GCP) including AI/ML services
- Understanding of API design, microservices, and event-driven architectures
- Familiarity with MLOps/LLMOps practices — model evaluation, observability, cost/latency monitoring
Preferred / Nice-to-Have
- Experience architecting agentic systems specifically for data ops / DataOps automation
- Exposure to data governance, lineage, and catalog tools (Collibra, Alation, Unity Catalog, etc.)
- Certifications in cloud architecture or AI/ML (AWS/Azure/GCP Solutions Architect, etc.)
- Experience with evaluation/guardrail frameworks for autonomous agents (e.g., agent testing harnesses, red-teaming for AI systems)
- Prior experience in a "platform automation" or "AI-driven operations" initiative
About Cognizant
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, provincial or local laws.
