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.)
Skills Required
- 15+ years experience in Data Engineering, Data Science, or AI
- 3+ years hands-on experience building LLM/GenAI solutions at scale
- Proven experience in architecture, solution design, and enterprise delivery
- Hands-on experience with LLMs (OpenAI, Claude) and RAG pipelines
- Experience with LangChain, LangGraph or similar agent frameworks
- Strong Python and PySpark engineering (production-grade)
- API development experience (FastAPI/Flask) and microservices
- Experience integrating with Fabric/Azure Databricks and Snowflake
- Cloud platform experience (Azure, AWS, or GCP) and SQL
- Familiarity with containers, CI/CD, monitoring, and observability
- Experience in data engineering (ETL/ELT), pipelines, and data governance
- Experience in ML lifecycle or NLP/data science
- Experience with hallucination reduction, safety, and evaluation frameworks
- Experience with enterprise GenAI deployments, security and privacy
- Fine-tuning techniques (LoRA, PEFT, prompt tuning, few-shot)
- Experience specifically with Azure ecosystem (Azure OpenAI, AI Search, Fabric)
- Exposure to industry use cases (Insurance, BFSI, Healthcare, Retail)
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
