AI Architect​/Principal AI SME – Enterprise & Industrial AI Platforms

Position: AI Architect / Principal AI SME – Enterprise & Industrial AI Platforms
Location: Bengaluru

Job Description
AI Architect / Principal AI SME – Enterprise & Industrial AI Platforms

Location:

India (Hyderabad / Bangalore)

Experience:

15–20+ Years
Business Unit:
Digital Engineering / Advanced Technology
Role Summary
Cyient is looking for a seasoned Data and AI Architect / Principal SME to lead the design and implementation of enterprise-scale Data and AI solutions. Experience in building and architecting Data and AI platforms/applications across various domains such as Aerospace, Energy, Mining, Manufacturing, Healthcare & Med Tech, Utilities, and Transportation domains is an added advantage.
This role demands hands-on architectural depth in either Azure or AWS or Both cloud platform with GenAI, LLMs, Agentic AI frameworks, distributed data platforms, Digital Twin ecosystems, and hybrid cloud deployments, combined with strategic technology leadership.
The candidate will act as a Chief Architect-level technical authority, driving Data & AI platform and application vision, reusable accelerators, IP creation, and domain-led AI transformation initiatives.
Core Responsibilities
Data Platform & Lakehouse Engineering
Design and govern modern data platforms:
Architecture Components:
Lakehouse architecture (Medallion architecture)
Delta tables & ACID transactional layers
Multi-tenant architecture with cost governance
Data mesh or federated data architecture
Technologies:
Databricks
Apache Spark (batch & streaming)
Delta Live Tables
Apache Druid
Dremio
Kubeflow pipelines
Airflow orchestration
Data Engineering Capabilities:
Schema evolution & versioning
Metadata & lineage management
Data quality frameworks
Dimensional modeling for analytics
Streaming ingestion (Kafka-based)
Enterprise AI & Agentic Architecture

• Architect enterprise-scale Agentic AI frameworks using:
Lang Graph
Model Context Protocol (MCP)
Multi-agent orchestration frameworks
Memory-driven AI systems

• Design and implement:
RAG pipelines (Hybrid RAG, Graph-RAG)
Embeddings pipeline (Open-source & enterprise models)
Prompt orchestration & guardrails
Fine-tuning pipelines (PEFT, LoRA, domain adaptation)

• Build secure LLM deployments (On-prem / Air-gapped / Cloud-agnostic).

• Define LLMOps lifecycle:
Evaluation harness
Hallucination detection
Observability (tracing, telemetry)
Model governance
Advanced AI/ML & Deep Learning

• Architect ML systems using:
Tensor Flow, Py Torch
Scikit-Learn, XGBoost
LSTM, CNN, Transformer models
Vision-Language Models (VLMs)

• Time-series forecasting & anomaly detection for industrial telemetry.

• Computer Vision pipelines:
YOLO-based detection
Semantic segmentation
Object tracking

• Model compression & edge deployment (quantization, pruning).

• Edge AI deployment on embedded hardware platforms.
Cloud, Dev Ops & Infrastructure

• Cloud-native AI architecture on:
Azure
AWS

• Containerization:
o Docker
o Kubernetes (Helm, Operators)

• Infrastructure as Code (Terraform exposure preferred)

• CI/CD for ML pipelines

• Secure Dev Sec Ops  integration

• Hybrid & on-prem deployments with compliance constraints.

Databases, Graph & Vector Systems

• RDBMS:
PostgreSQL

• No

SQL:
MongoDB

• Graph Databases:
Neo4j (Ontology & Knowledge Graph modeling)

• Vector Databases:
o Pinecone / FAISS / Milvus / Enterprise Vector DBs

• Context modeling and semantic search frameworks.
Architecture Governance & Innovation

• Conduct architecture reviews and technical due diligence (M&A context).

• Define reusable AI platform blueprints and accelerators.

• Lead patentable innovations & IP creation.

• Mentor architects and senior engineers.

• Engage with CXOs for AI roadmap definition.
Domain-Specific AI Applications
Drive AI programs across Cyient industry verticals:

• Aerospace – Fuel analytics, predictive maintenance, digital twin simulation

• Energy & Utilities – Smart grid analytics, leak detection, asset health

• Oil & Gas – Production intelligence, APM

• Semiconductor – Tool matching, fab analytics, defect classification

• Manufacturing – Process optimization, time-series anomaly detection

• Buildings – Smart HVAC optimization & control analytics

• Transportation – Vision-based traffic & infrastructure analytics

Required Experience

• 15+ years in Data, AI, and Platform Engineering.

• 5+ years in AI Architecture leadership role.

• Proven delivery of enterprise-scale AI platforms.

• Experience in industrial / engineering AI ecosystems.

• Strong background in distributed systems and scalable data processing.

Educational Background

•

B.Tech / BE in Computer Science or related field

• M.Tech / MS in Data Science / AI (Preferred)

What Makes This Role Strategic for Cyient
This role anchors Cyient’s ambition to build:

• Cloud-agnostic AI platforms

• Industrial-grade Agentic AI systems

• Digital Twin–driven engineering intelligence

• Secure enterprise LLM deployments
The AI Architect will directly influence AI-led engineering transformation across global customers.