Architect AI Data Engineer
Reads like a ML Engineer role: putting models into production.
Gurugram, Haryana, India
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDefine end-to-end architecture for enterprise GenAI platforms and use cases
Design scalable single-agent and multi-agent systems
Build enterprise-grade RAG pipelines focused on retrieval accuracy
Lead development of GenAI platforms, APIs, and microservices
Define and enforce LLM guardrails for hallucination and safety
From the employer’s posting
Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
Build enterprise-grade RAG pipelines with strong focus on retrieval accuracy and evaluation
Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
What you’ll bring
All qualificationsCore experience
- 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Hands-on experience with Azure / AWS / GCP
- Prior experience in one or more:
- Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
- 12–15 years total experience, with 3+ years in GenAI / LLM-based systems
Qualification wording
12–15 years total experience, with 3+ years in GenAI / LLM-based systems
Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
Hands-on experience with Azure / AWS / GCP
Prior experience in one or more:
Experience with Azure AI stack (Azure OpenAI, Cognitive Search)
Tools in this posting
- Python
- SQL
- AWS
- Azure
- Databricks
- Docker
- GCP
- Kubernetes
- Snowflake
- FastAPI
- Flask
- PySpark
Source — Tool mentions in context
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
- Cloud platforms (Azure/AWS/GCP) - SQL - Containers, CI/CD, monitoring
- Good exposure to: - Cloud platforms (Azure/AWS/GCP) - SQL
Cloud & Platform - Hands-on experience with Azure / AWS / GCP - Familiarity with:
- Deep data analysis experience and handling large volume of data - Fabric/Azure Databricks/Snowflake data engineering integration skills - Good exposure to:
- 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
- Familiarity with: - Containers (Docker/Kubernetes) - CI/CD pipelines
3. Platform & Engineering Excellence - Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.) - Define engineering best practices: coding standards, testing, packaging, observability
Posting record
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- Live
- First seen by us
- Aug 12, 2026 · 78 days ago
- Recorded sightings
- 331
- Last seen by us
- Oct 29, 2026
- Employer says posted
- Jul 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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- Ask the employer about the pay range before committing time to the process.
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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)
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)
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Gurugram, Haryana, India
- 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
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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