AI Architect

Responsibilities

Agentic AI Systems

• 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

Requirements

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
• B.Tech/BE in Computer Science or related field; M.Tech/MS in Data Science or AI preferred