AI Architect

Design reusable patterns for Agentic AI systems including RAG, Multi-Agent Orchestration, and Human-in-the-loop systems

Define how different agents communicate, share state, and hand off tasks to one another

Architect long-term and episodic memory layers using Vector Databases, embedding pipelines, and knowledge graphs

Decide when to use high-reasoning models vs. worker models to optimise cost and performance

Predict and control token usage architect systems with semantic caching to prevent redundant LLM spend

Set architectural standards for explainability, auditability, and guardrails to prevent hallucinations and bias

Ensure data governance, privacy compliance, and responsible AI practices across all systems

AI Infrastructure & MLOps

Design scalable AI infrastructure including model serving, inference architecture, AI microservices, and APIs

Architect distributed systems supporting AI workloads

Define MLOps and CI/CD pipelines for AI systems

Architect containerised and cloud-native deployments design monitoring and observability for AI services

Optimise for cost, performance, and scalability across the AI stack

Enterprise AI & Agentic Architecture

Architect enterprise-scale Agentic AI frameworks using LangGraph, Model Context Protocol (MCP), multi-agent orchestration frameworks, and memory-driven AI systems

Design and implement RAG pipelines (Hybrid RAG, Graph-RAG), embeddings pipelines (open-source and enterprise models), prompt orchestration, guardrails, and fine-tuning pipelines (PEFT, LoRA, domain adaptation)

Build secure LLM deployments across on-prem, air-gapped, and cloud-agnostic environments

Define LLMOps lifecycle covering evaluation harness, hallucination detection, observability (tracing, telemetry), and model governance

Hands-on experience with agentic AI frameworks - LangChain, LlamaIndex, AutoGen, CrewAI

Data Platform & Lakehouse Engineering

Design and govern modern data platforms built on Medallion (Bronze-Silver-Gold) architecture with Delta tables and ACID transactional layers

Architect multi-tenant platforms with cost governance and data mesh or federated data architecture patterns

Work across the core stack: Databricks, Apache Spark (batch & streaming), Delta Live Tables, Apache Druid, Dremio, Kubeflow Pipelines, Airflow

Drive schema evolution and versioning, metadata and lineage management, data quality frameworks, dimensional modelling for analytics, and Kafka-based streaming ingestion

Advanced AI/ML & Deep Learning

Architect ML systems using TensorFlow, PyTorch, Scikit-Learn, XGBoost, LSTM, CNN, Transformer models, and Vision-Language Models (VLMs)

Design time-series forecasting and anomaly detection solutions for industrial telemetry

Cloud, Infrastructure & DevOps

Cloud-native AI architecture on Azure and AWS

Containerisation using Docker and Kubernetes (Helm, Operators)

Infrastructure as Code using Terraform

CI/CD for ML pipelines with secure DevSecOps integration

Hybrid and on-prem deployments under compliance constraints

Databases, Graph & Vector Systems

RDBMS: PostgreSQL NoSQL: MongoDB

Graph Databases: Neo4j for ontology and knowledge graph modelling

Vector Databases: Pinecone, FAISS, Milvus, and enterprise vector DB solutions

Context modelling and semantic search frameworks

Required Experience

15+ years in Data, AI, and Platform Engineering

5+ years in an AI Architecture leadership role

Proven delivery of enterprise-scale AI platforms in production environments

Experience in industrial or engineering AI ecosystems

Strong background in distributed systems and scalable data processing