Architect AI Data Engineer

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

1. 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

1. 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

1. 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)

1. 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

1. 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

1. 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

1. 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

1. 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)

1. 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

1. 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

1. 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

1. 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

1. 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)

1. 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

1. 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)