Architect, AI Data Engineer

Role Overview

• 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
• 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
• 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
• 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)
• Partner with Data Engineering teams on Data ingestion, pipelines, and quality controls
• Work with business stakeholders to identify high-value GenAI use cases and translate business problems into AI-driven solutions
• 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

Requirements

• 12–15 years total experience
• 3+ years in GenAI / LLM-based systems
• Proven experience in leading architecture and delivery of enterprise solutions
• Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
• RAG pipelines and retrieval optimisation
• Experience with LangChain, LangGraph, or similar frameworks
• Agent orchestration and tool-calling architectures
• Strong Python/Pyspark engineering expertise with proven API integration experience
• Deep data analysis experience and handling large volume of data
• Fabric/Azure Databricks/Snowflake data engineering integration skills
• Cloud platforms (Azure/AWS/GCP)
• Familiarity with Containers (Docker/Kubernetes), CI/CD pipelines, Monitoring & observability
• Data Engineering (ETL/ELT, pipelines, orchestration)
• Data Science / ML lifecycle (especially NLP)
• Analytics engineering / data products
• Fine-tuning techniques (LoRA, PEFT, prompt tuning)

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• ETL
• Flask
• Google Cloud Platform
• Kubernetes
• Microservices
• PySpark
• Python

Benefits

• Health insurance
• 401(k) matching
• Flexible work hours
• Paid time off
• Remote work options