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)