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.)