# Principal Architect AI Data Engineer

Source: https://realanalystjobs.com/job/principal-architect-ai-data-engineer-exl-6d9678e5
Principal Architect AI Data Engineer at EXL, an on-site role based in Gurugram, India. Experience level: 15+ Years. Posted on 4 July 2026, read from EXL's own careers page (Oracle HCM), which RealAnalystJobs re-checks daily and removes the role when it closes.

Updated 15 November 2026.

## Key facts

- Company: [EXL](https://realanalystjobs.com/companies/exl)
- Location: Gurugram, India
- Work mode: On-site
- Posted: Jul 4, 2026
- Employment type: Full time
- Experience level: 15+ Years
- Source: EXL's careers page (Oracle HCM), re-checked daily

Skills: [SQL](https://realanalystjobs.com/skills/sql), [Python](https://realanalystjobs.com/skills/python), Snowflake, AWS, Azure, Spark

## Visa sponsorship

The posting does not say.

EXL had H-1B petitions approved for 349 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/exl#h1b-record)

## About this role

Key Responsibilities

Architecture & Solution Leadership

• Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).

• Define reference architectures, reusable frameworks, and best practices for LLM applications across the organisation.

• Architect and oversee implementation of end-to-end RAG pipelines :

• Data ingestion → chunking → embeddings → vector search → orchestration → response synthesis.

• Drive scalability, reliability, cost optimisation, and performance across GenAI platforms.

Agentic & LLM Engineering (Hands-on + Oversight)

• Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows (LangChain, LangGraph, etc.).

• Guide teams on tool-calling, function-calling, memory handling, and multi-agent system design .

• Lead efforts in hallucination reduction, guardrails, safety mechanisms, and output evaluation frameworks .

Platform & Engineering Excellence

• Architect production-grade APIs and services (FastAPI/Flask/enterprise microservices) for LLM solutions.

• Define MLOps / LLMOps pipelines including CI/CD, monitoring, observability, and evaluation.

• Partner with Data Engineering teams to ensure:

• Data quality, lineage, governance, and compliance

• Seamless integration with enterprise data platforms

Organisation-Level Responsibilities (Critical)

Capability Building & CoE Development

• Build and scale GenAI / Agentic AI Centre of Excellence (CoE) .

• Define standardised frameworks, accelerators, and reusable components to improve delivery velocity.

• Drive organisation-wide adoption of GenAI best practices and tooling standards .

Strategic & Stakeholder Leadership

• Engage with CXOs, business stakeholders, and clients to translate business problems into AI-led solutions.

• Lead solutioning, pre-sales, RFP responses, and client workshops for GenAI opportunities.

• Influence AI strategy, roadmap, and investment decisions at organisational level.

Governance, Risk & Compliance

• Establish enterprise governance frameworks for GenAI:

• Responsible AI, security, privacy, ethical usage, and compliance

• Define policies for:

• Data access, redaction, model usage, auditability, and explainability

Mentorship & Team Leadership

• Mentor and guide architects, engineers, and data scientists .

• Drive technical upskilling, hiring strategy, and capability maturity .

• Review solution designs and enforce architecture quality standards .

Experience & Must-Have Skills

Experience

• 15+ years of total experience in Data Engineering / Data Science / AI

• 3+ years of hands-on experience in LLM / GenAI solutions at scale

• Proven experience in architecture, solution design, and enterprise delivery

LLM / GenAI & Agentic Engineering

• Strong hands-on experience with:

• LLMs (Claude, OpenAI, etc.)

• RAG pipelines and retrieval optimisation

• GPT + Agentic AI implementation experience

• Experience with:

• LangChain, LangGraph, or similar frameworks

• Agent orchestration and tool-calling architectures

• Deep understanding of:

• LLM limitations, evaluation, and optimisation strategies

Core Engineering

• Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience

• Deep data analysis experience and handling large volume of data

• Fabric/Azure Databricks/Snowflake data engineering integration skills

• Good exposure to:

• Cloud platforms (Azure/AWS/GCP)

• SQL

• Containers, CI/CD, monitoring

Data / AI Foundations (Mandatory)

Prior experience in one or more:

• Data Engineering (ETL/ELT, pipelines, orchestration)

• Data Science / ML lifecycle (especially NLP)

Analytics engineering / data products

Good-to-Have / Preferred

• Fine-tuning techniques ( LoRA, PEFT, prompt tuning, few-shot learning )

• Experience with enterprise GenAI deployments (security, privacy, governance)

• Experience with Azure ecosystem (Azure OpenAI, AI Search, Fabric, etc.)

• Exposure to industry use cases (Insurance, BFSI, Healthcare, Retail, etc.)

Key Responsibilities

Architecture & Solution Leadership

• Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).

• Define reference architectures, reusable frameworks, and best practices for LLM applications across the organisation.

• Architect and oversee implementation of end-to-end RAG pipelines :

• Data ingestion → chunking → embeddings → vector search → orchestration → response synthesis.

• Drive scalability, reliability, cost optimisation, and performance across GenAI platforms.

Agentic & LLM Engineering (Hands-on + Oversight)

• Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows (LangChain, LangGraph, etc.).

• Guide teams on tool-calling, function-calling, memory handling, and multi-agent system design .

• Lead efforts in hallucination reduction, guardrails, safety mechanisms, and output evaluation frameworks .

Platform & Engineering Excellence

• Architect production-grade APIs and services (FastAPI/Flask/enterprise microservices) for LLM solutions.

• Define MLOps / LLMOps pipelines including CI/CD, monitoring, observability, and evaluation.

• Partner with Data Engineering teams to ensure:

• Data quality, lineage, governance, and compliance

• Seamless integration with enterprise data platforms

Organisation-Level Responsibilities (Critical)

Capability Building & CoE Development

• Build and scale GenAI / Agentic AI Centre of Excellence (CoE) .

• Define standardised frameworks, accelerators, and reusable components to improve delivery velocity.

• Drive organisation-wide adoption of GenAI best practices and tooling standards .

Strategic & Stakeholder Leadership

• Engage with CXOs, business stakeholders, and clients to translate business problems into AI-led solutions.

• Lead solutioning, pre-sales, RFP responses, and client workshops for GenAI opportunities

Description from EXL's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you.

## How to apply

Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/principal-architect-ai-data-engineer-exl-6d9678e5) and use Apply now there to reach the employer's application. The listing is re-checked against EXL's careers page daily and removed when it closes.

## EXL

117 open analyst roles on Real Analyst Jobs. [More roles at EXL](https://realanalystjobs.com/companies/exl)

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## Sources and links

- [This role on Real Analyst Jobs](https://realanalystjobs.com/job/principal-architect-ai-data-engineer-exl-6d9678e5)
- [EXL on Real Analyst Jobs](https://realanalystjobs.com/companies/exl)
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