Architect AI Data Engineer

Job Description: 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)

Responsibilities: 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)

Qualifications: 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)