Job Title

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 & 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: LangGraph 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: TensorFlow, PyTorch 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, DevOps & 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 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 analyticsRequired 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.