The Role
Design and lead end-to-end enterprise GenAI architectures and agentic systems, build production-grade RAG pipelines and APIs, establish prompt and evaluation standards, integrate GenAI with enterprise data platforms, enforce Responsible AI guardrails, and provide technical leadership and stakeholder solutioning for scalable, secure deployments.
Summary Generated by Built In
Key Responsibilities
1. Solution Architecture & Strategy
- Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
- Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
- Establish reference architectures, design patterns, and reusable frameworks
- Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
- Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
2. Agentic AI & LLM Engineering Leadership
- Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
- Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
- Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
- Optimise solutions for latency, cost, scalability, and reliability
3. Platform & Engineering Excellence
- Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
- Define engineering best practices: coding standards, testing, packaging, observability
- Ensure seamless integration with enterprise data platforms, APIs, and business applications
- Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
4. Governance, Risk & Responsible AI
- Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
- Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
- Ensure compliance with data security, privacy, and enterprise governance standards
- Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
5. Data & Ecosystem Collaboration
- Partner with Data Engineering teams on:
- Data ingestion, pipelines, and quality controls
- Metadata management and knowledge graph strategies
- Work with business stakeholders to:
- Identify high-value GenAI use cases
- Translate business problems into AI-driven solutions
6. Leadership & Stakeholder Management
- Provide technical leadership and mentorship to engineering teams
- Act as a solution advisor to clients/stakeholders (including pre-sales, PoCs, solutioning)
- Present architecture and design decisions to senior leadership and CXOs
- Drive COE initiatives, knowledge sharing, and internal capability building
Must-Have Skills & Experience
Experience
- 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
- Proven experience in leading architecture and delivery of enterprise solutions
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
Cloud & Platform
- Hands-on experience with Azure / AWS / GCP
- Familiarity with:
- Containers (Docker/Kubernetes)
- CI/CD pipelines
- Monitoring & observability
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)
- Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
- Knowledge of knowledge graphs, semantic layers, or enterprise search
- Experience in domain-specific GenAI solutions (Insurance, BFSI, Healthcare)
Key Responsibilities
1. Solution Architecture & Strategy
- Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
- Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
- Establish reference architectures, design patterns, and reusable frameworks
- Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
- Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
2. Agentic AI & LLM Engineering Leadership
- Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
- Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
- Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
- Optimise solutions for latency, cost, scalability, and reliability
3. Platform & Engineering Excellence
- Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
- Define engineering best practices: coding standards, testing, packaging, observability
- Ensure seamless integration with enterprise data platforms, APIs, and business applications
- Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
4. Governance, Risk & Responsible AI
- Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
- Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
- Ensure compliance with data security, privacy, and enterprise governance standards
- Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
5. Data & Ecosystem Collaboration
- Partner with Data Engineering teams on:
- Data ingestion, pipelines, and quality controls
- Metadata management and knowledge graph strategies
- Work with business stakeholders to:
- Identify high-value GenAI use cases
- Translate business problems into AI-driven solutions
6. Leadership & Stakeholder Management
- Provide technical leadership and mentorship to engineering teams
- Act as a solution advisor to clients/stakeholders (including pre-sales, PoCs, solutioning)
- Present architecture and design decisions to senior leadership and CXOs
- Drive COE initiatives, knowledge sharing, and internal capability building
Must-Have Skills & Experience
Experience
- 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
- Proven experience in leading architecture and delivery of enterprise solutions
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
Cloud & Platform
- Hands-on experience with Azure / AWS / GCP
- Familiarity with:
- Containers (Docker/Kubernetes)
- CI/CD pipelines
- Monitoring & observability
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)
- Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
- Knowledge of knowledge graphs, semantic layers, or enterprise search
- Experience in domain-specific GenAI solutions (Insurance, BFSI, Healthcare)
Key Responsibilities
1. Solution Architecture & Strategy
- Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
- Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
- Establish reference architectures, design patterns, and reusable frameworks
- Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
- Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
2. Agentic AI & LLM Engineering Leadership
- Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
- Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
- Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
- Optimise solutions for latency, cost, scalability, and reliability
3. Platform & Engineering Excellence
- Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
- Define engineering best practices: coding standards, testing, packaging, observability
- Ensure seamless integration with enterprise data platforms, APIs, and business applications
- Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
4. Governance, Risk & Responsible AI
- Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
- Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
- Ensure compliance with data security, privacy, and enterprise governance standards
- Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
5. Data & Ecosystem Collaboration
- Partner with Data Engineering teams on:
- Data ingestion, pipelines, and quality controls
- Metadata management and knowledge graph strategies
- Work with business stakeholders to:
- Identify high-value GenAI use cases
- Translate business problems into AI-driven solutions
6. Leadership & Stakeholder Management
- Provide technical leadership and mentorship to engineering teams
- Act as a solution advisor to clients/stakeholders (including pre-sales, PoCs, solutioning)
- Present architecture and design decisions to senior leadership and CXOs
- Drive COE initiatives, knowledge sharing, and internal capability building
Must-Have Skills & Experience
Experience
- 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
- Proven experience in leading architecture and delivery of enterprise solutions
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
Cloud & Platform
- Hands-on experience with Azure / AWS / GCP
- Familiarity with:
- Containers (Docker/Kubernetes)
- CI/CD pipelines
- Monitoring & observability
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)
- Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
- Knowledge of knowledge graphs, semantic layers, or enterprise search
- Experience in domain-specific GenAI solutions (Insurance, BFSI, Healthcare)
Skills Required
- 12-15 years total experience with 3+ years in GenAI / LLM-based systems
- Proven experience leading architecture and delivery of enterprise solutions
- Hands-on experience with LLMs (Claude, OpenAI, GPT) and understanding of LLM limitations and optimization
- Design and build RAG pipelines, retrieval optimization, and evaluation frameworks
- Experience implementing agentic AI, agent orchestration, tool-calling and function orchestration
- Experience with LangChain, LangGraph, or similar frameworks
- Strong Python and PySpark engineering expertise with production-grade development and API integration experience
- Deep data analysis experience and handling large volumes of data
- Experience integrating with Microsoft Fabric, Azure Databricks, or Snowflake
- Hands-on experience with cloud platforms (Azure, AWS, GCP)
- Strong SQL skills
- Experience with containers (Docker, Kubernetes), CI/CD pipelines, and monitoring/observability
- Prior experience in one or more: Data Engineering, Data Science/ML lifecycle (especially NLP), or Analytics Engineering / data products
- Technical leadership, mentoring, stakeholder engagement, and presenting to senior leadership/CXOs
- Fine-tuning techniques (LoRA, PEFT, prompt tuning)
- Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
- Knowledge of knowledge graphs, semantic layers, or enterprise search
- Experience building domain-specific GenAI solutions (Insurance, BFSI, Healthcare)
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The Company
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.
