Principal Architect AI Data Engineer

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.)