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

Job Description

AI Architect / Principal AI SME Enterprise & Industrial AI Platforms

Location: India (Hyderabad / Bangalore)

Experience: 1520+ 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 & MedTech, 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:

1. LangGraph
2. Model Context Protocol (MCP)
3. Multi-agent orchestration frameworks
4. Memory-driven AI systems

Design and implement:

1. RAG pipelines (Hybrid RAG, Graph-RAG)
2. Embeddings pipeline (Open-source & enterprise models)
3. Prompt orchestration & guardrails
4. Fine-tuning pipelines (PEFT, LoRA, domain adaptation)

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

Define LLMOps lifecycle:

1. Evaluation harness
2. Hallucination detection
3. Observability (tracing, telemetry)
4. Model governance

Advanced AI/ML & Deep Learning

Architect ML systems using:

1. TensorFlow, PyTorch
2. Scikit-Learn, XGBoost
3. LSTM, CNN, Transformer models
4. Vision-Language Models (VLMs)

Time-series forecasting & anomaly detection for industrial telemetry.

Computer Vision pipelines:

1. YOLO-based detection
2. Semantic segmentation
3. Object tracking

Model compression & edge deployment (quantization, pruning).

Edge AI deployment on embedded hardware platforms.

Cloud, DevOps & Infrastructure

Cloud-native AI architecture on:

1. Azure
2. AWS

Containerization:

o Docker

o Kubernetes (Helm, Operators)

Infrastructure as Code (Terraform exposure preferred)

CI/CD for ML pipelines

Secure DevSecOps integration

Hybrid & on-prem deployments with compliance constraints.

Databases, Graph & Vector Systems

RDBMS: PostgreSQL

NoSQL: 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 Cyients ambition to build:

Cloud-agnostic AI platforms

Industrial-grade Agentic AI systems

Digital Twindriven engineering intelligence

Secure enterprise LLM deployments

The AI Architect will directly influence AI-led engineering transformation across global customers.