Principal Architect AI Data Engineer
Hybrid Gurugram Full-time Senior Data Engineering
At a glance
Senior Data Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Data Engineering salary
RoleData Engineering
SenioritySenior
LocationGurugram
WorkplaceHybrid
EmploymentFull-time
PostedJul 4, 2026
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Set architecture for GenAI and agent-based systems, then steer delivery of retrieval pipelines from data intake through answer generation
- Guide prompt and agent design, including tool use, memory, safety controls, and checks for output quality
- Shape production APIs and LLMOps practices for deployment, monitoring, evaluation, and reliable performance
- Build a GenAI centre of excellence with shared frameworks, reusable components, and organisation-wide standards
- Turn client and business needs into AI solutions, while advising on strategy, governance, and team development
What you bring
- At least fifteen years in data engineering, data science, or AI, including three years building LLM or GenAI solutions at scale
- A track record of enterprise architecture and delivery, with hands-on RAG, agentic AI, and orchestration experience
- Strong production Python or PySpark skills, API integration experience, and data engineering platform integration
- Background in data engineering, ML or data science, or analytics engineering, plus knowledge of LLM evaluation and optimisation
Who this fits
This senior architecture role suits someone who can combine hands-on GenAI engineering with organisation-level technical leadership. You'll work with executives, clients, and technical teams, and will mentor specialists and guide hiring and capability building. The role is based in Gurugram on a hybrid schedule.
From the employer
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.
- 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.
- 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.)
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