Key Responsibilities
1. Solution Architecture & Technical Leadership
- Architect enterprise-grade agentic and LLM solutions (single-agent, multi-agent, tool-driven workflows)
- Define scalable GenAI system design patterns (RAG, orchestration layers, evaluation frameworks)
- Act as the technical anchor for GenAI initiatives across projects
- Drive design reviews, architecture governance, and best practices
2. Agentic AI & LLM Engineering
- Design and build agentic systems using LLMs for use cases such as:
- Knowledge assistants
- Document automation & intelligence
- Workflow orchestration
- Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains
- Build tool-calling / function-calling frameworks for agent workflows
3. RAG & Retrieval Systems
- Lead end-to-end implementation of RAG pipelines:
- Data ingestion → chunking → embeddings → vector indexing → retrieval → response generation
- Optimise retrieval quality (recall, relevance, grounding)
- Evaluate and benchmark different architectures
4. Productisation & Engineering Excellence
- Develop production-grade APIs/services (FastAPI, Flask, etc.)
- Drive code quality, testing standards, and reusable architecture components
- Ensure solutions are performance optimised (latency, cost, reliability)
5. Governance, Safety & Evaluation
- Implement LLM guardrails:
- Hallucination control
- Safety filters
- Policy enforcement
- Define evaluation frameworks:
- Response quality metrics
- RAG benchmarking
- Human-in-the-loop validation
6. Collaboration & Delivery Leadership
- Partner with:
- Data Engineering → pipelines, data quality, governance
- MLOps → deployment, CI/CD, monitoring
- Business/Product → use-case alignment
- Drive end-to-end delivery ownership across multiple projects
7. Technical Leadership Responsibilities (Critical Addition)
- Mentor and guide junior engineers and project teams
- Conduct technical reviews, solution walkthroughs, and code reviews
- Support pre-sales / RFPs / solution proposals with architecture inputs
- Drive reusable accelerators, frameworks, and COE assets
- Stay ahead of industry evolution and help shape EXL’s GenAI strategy
- Influence technology choice, design decisions, and roadmap planning
Must-Have Skills
Experience
- 9–12 years total experience
- 2–4+ years hands-on in LLM / GenAI delivery (production use cases)
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
Leadership Capabilities
- Experience leading solution design or small teams
- Ability to translate business problems into AI solutions
- Strong stakeholder communication and influencing skills
Good-to-Have / Preferred
- Fine-tuning approaches: LoRA / PEFT / prompt tuning
- Experience with Azure AI stack (Azure OpenAI, AI Search)
- Exposure to:
- Enterprise security & data privacy in GenAI
- Coding agents / autonomous agent frameworks
Experience in insurance / BFSI domains (valuable for EXL use cases)
ResponsibilitiesKey Responsibilities
1. Solution Architecture & Technical Leadership
- Architect enterprise-grade agentic and LLM solutions (single-agent, multi-agent, tool-driven workflows)
- Define scalable GenAI system design patterns (RAG, orchestration layers, evaluation frameworks)
- Act as the technical anchor for GenAI initiatives across projects
- Drive design reviews, architecture governance, and best practices
2. Agentic AI & LLM Engineering
- Design and build agentic systems using LLMs for use cases such as:
- Knowledge assistants
- Document automation & intelligence
- Workflow orchestration
- Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains
- Build tool-calling / function-calling frameworks for agent workflows
3. RAG & Retrieval Systems
- Lead end-to-end implementation of RAG pipelines:
- Data ingestion → chunking → embeddings → vector indexing → retrieval → response generation
- Optimise retrieval quality (recall, relevance, grounding)
- Evaluate and benchmark different architectures
4. Productisation & Engineering Excellence
- Develop production-grade APIs/services (FastAPI, Flask, etc.)
- Drive code quality, testing standards, and reusable architecture components
- Ensure solutions are performance optimised (latency, cost, reliability)
5. Governance, Safety & Evaluation
- Implement LLM guardrails:
- Hallucination control
- Safety filters
- Policy enforcement
- Define evaluation frameworks:
- Response quality metrics
- RAG benchmarking
- Human-in-the-loop validation
6. Collaboration & Delivery Leadership
- Partner with:
- Data Engineering → pipelines, data quality, governance
- MLOps → deployment, CI/CD, monitoring
- Business/Product → use-case alignment
- Drive end-to-end delivery ownership across multiple projects
7. Technical Leadership Responsibilities (Critical Addition)
- Mentor and guide junior engineers and project teams
- Conduct technical reviews, solution walkthroughs, and code reviews
- Support pre-sales / RFPs / solution proposals with architecture inputs
- Drive reusable accelerators, frameworks, and COE assets
- Stay ahead of industry evolution and help shape EXL’s GenAI strategy
- Influence technology choice, design decisions, and roadmap planning
Must-Have Skills
Experience
- 9–12 years total experience
- 2–4+ years hands-on in LLM / GenAI delivery (production use cases)
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
Leadership Capabilities
- Experience leading solution design or small teams
- Ability to translate business problems into AI solutions
- Strong stakeholder communication and influencing skills
Good-to-Have / Preferred
- Fine-tuning approaches: LoRA / PEFT / prompt tuning
- Experience with Azure AI stack (Azure OpenAI, AI Search)
- Exposure to:
- Enterprise security & data privacy in GenAI
- Coding agents / autonomous agent frameworks
Experience in insurance / BFSI domains (valuable for EXL use cases)
QualificationsKey Responsibilities
1. Solution Architecture & Technical Leadership
- Architect enterprise-grade agentic and LLM solutions (single-agent, multi-agent, tool-driven workflows)
- Define scalable GenAI system design patterns (RAG, orchestration layers, evaluation frameworks)
- Act as the technical anchor for GenAI initiatives across projects
- Drive design reviews, architecture governance, and best practices
2. Agentic AI & LLM Engineering
- Design and build agentic systems using LLMs for use cases such as:
- Knowledge assistants
- Document automation & intelligence
- Workflow orchestration
- Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains
- Build tool-calling / function-calling frameworks for agent workflows
3. RAG & Retrieval Systems
- Lead end-to-end implementation of RAG pipelines:
- Data ingestion → chunking → embeddings → vector indexing → retrieval → response generation
- Optimise retrieval quality (recall, relevance, grounding)
- Evaluate and benchmark different architectures
4. Productisation & Engineering Excellence
- Develop production-grade APIs/services (FastAPI, Flask, etc.)
- Drive code quality, testing standards, and reusable architecture components
- Ensure solutions are performance optimised (latency, cost, reliability)
5. Governance, Safety & Evaluation
- Implement LLM guardrails:
- Hallucination control
- Safety filters
- Policy enforcement
- Define evaluation frameworks:
- Response quality metrics
- RAG benchmarking
- Human-in-the-loop validation
6. Collaboration & Delivery Leadership
- Partner with:
- Data Engineering → pipelines, data quality, governance
- MLOps → deployment, CI/CD, monitoring
- Business/Product → use-case alignment
- Drive end-to-end delivery ownership across multiple projects
7. Technical Leadership Responsibilities (Critical Addition)
- Mentor and guide junior engineers and project teams
- Conduct technical reviews, solution walkthroughs, and code reviews
- Support pre-sales / RFPs / solution proposals with architecture inputs
- Drive reusable accelerators, frameworks, and COE assets
- Stay ahead of industry evolution and help shape EXL’s GenAI strategy
- Influence technology choice, design decisions, and roadmap planning
Must-Have Skills
Experience
- 9–12 years total experience
- 2–4+ years hands-on in LLM / GenAI delivery (production use cases)
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
Leadership Capabilities
- Experience leading solution design or small teams
- Ability to translate business problems into AI solutions
- Strong stakeholder communication and influencing skills
Good-to-Have / Preferred
- Fine-tuning approaches: LoRA / PEFT / prompt tuning
- Experience with Azure AI stack (Azure OpenAI, AI Search)
- Exposure to:
- Enterprise security & data privacy in GenAI
- Coding agents / autonomous agent frameworks
Experience in insurance / BFSI domains (valuable for EXL use cases)
Skills Required
- 9-12 years total professional experience
- 2-4+ years hands-on LLM/GenAI production delivery
- Hands-on experience with LLMs (Claude, OpenAI, etc.)
- Design and implementation of RAG pipelines and retrieval optimization
- GPT and agentic AI implementation experience (single/multi-agent, tool-calling)
- Experience with LangChain, LangGraph, or similar frameworks
- Advanced prompt engineering, prompt orchestration and reasoning chains
- Build production-grade APIs/services (FastAPI, Flask)
- Strong Python and PySpark engineering expertise with API integration experience
- Fabric / Azure Databricks / Snowflake data engineering integration skills
- Experience with cloud platforms (Azure, AWS, GCP)
- SQL proficiency and deep data analysis / handling large volumes of data
- Containers, CI/CD and monitoring experience
- Prior experience in Data Engineering, Data Science (NLP) or Analytics Engineering
- Experience leading solution design or small teams and mentoring junior engineers
- Fine-tuning approaches (LoRA, PEFT, prompt tuning)
- Experience with Azure AI stack (Azure OpenAI, AI Search)
- Exposure to enterprise security and data privacy for GenAI
- Experience coding agents / autonomous agent frameworks
- Experience in insurance / BFSI domains
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.
