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)