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

Databases, Graph & Vector Systems

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

AI Architect

Key skills