{"id":3744064,"url":"https://alion.io/job/cyient-ai-architect","title":"AI Architect","company":{"id":59344,"name":"Cyient","domain":"cyient.com","url":"https://alion.io/company/cyient","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zwayam","truth_index":{"grade":"A","score":87,"open_postings":23,"ghost_share":0,"stale_share":0.522,"repost_share":0,"time_to_fill_p50_days":46,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Databricks","optional":false},{"name":"Digital Twin","optional":false},{"name":"Docker","optional":false},{"name":"Edge AI","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"OCR","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"VLM","optional":false}],"status":"live","first_seen_at":"2026-08-13T17:01:23Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-04T12:32:06Z","board_verified":true,"closed_at":null,"days_open":82,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":82},"description":"We are seeking an accomplished AI Architect with 15+ years of technology experience to define and deliver enterprise-scale AI solutions spanning machine learning, Generative AI, Agentic AI, engineering intelligence, and CAD drawing understanding. The role combines architecture leadership with hands-on technical depth to convert complex business and engineering problems into secure, scalable, production-ready solutions across cloud, on-premises, hybrid, and edge environments.\nRole mandate: Own the journey from business problem and engineering data to AI architecture, PoC, industrialization, governance, and measurable value.\nKey Responsibilities\nDefine end-to-end enterprise architectures for AI/ML, Generative AI, Agentic AI, computer vision, multimodal AI, NLP, forecasting, and predictive analytics.\nArchitect agentic AI solutions with planners, specialized agents, tool use, memory, orchestration, human-in-the-loop controls, guardrails, and observability.\nDesign AI pipelines for CAD and engineering drawings, including 2D/3D CAD, P&IDs, schematics, layouts, technical diagrams, and associated metadata.\nLead approaches for drawing ingestion, raster/vector processing, OCR, symbol detection, object and relationship extraction, drawing classification, redline/change detection, and engineering document retrieval.\nIntegrate AI with CAD/PLM/BIM/GIS/document-management ecosystems and engineering formats such as DWG, DXF, DGN, IFC, STEP, IGES, PDF, and image-based drawings, where applicable.\nDesign RAG and knowledge-driven solutions using embeddings, vector databases, graph databases, knowledge graphs, semantic search, and enterprise content repositories.\nEvaluate and select models, vision-language models, frameworks, model-serving platforms, vector stores, graph technologies, and cloud or on-premises AI services.\nDefine scalable AI platforms covering ingestion, data preparation, feature/model development, APIs, inference, workflow orchestration, monitoring, and enterprise integration.\nEstablish MLOps and LLMOps practices covering experiment tracking, evaluation, model and prompt versioning, deployment, monitoring, drift, feedback, and governance.\nProvide guidance on GPU infrastructure, edge AI, model compression, quantization, pruning, inference optimization, latency, throughput, and cost.\nEnsure security, privacy, responsible AI, intellectual-property protection, explainability, reliability, and regulatory compliance are embedded by design.\nLead architecture reviews, customer workshops, technology evaluations, PoCs, proposal solutioning, estimation, and technical defense discussions.\nMentor AI engineers, data scientists, ML engineers, software engineers, and solution architects across multiple delivery teams.\nCore Architecture Domains\nDomain\nExpected Capability\nGenerative & Agentic AI\nLLMs, RAG, multimodal AI, autonomous and semi-autonomous agents, multi-agent orchestration, tool integration, memory, evaluation, guardrails, and human oversight.\nCAD & Engineering AI\nCAD and drawing understanding, OCR, vision-language models, geometry and symbol extraction, BOM/tag linkage, P&ID intelligence, change detection, drawing search, and engineering knowledge graphs.\nAI/ML & Analytics\nMachine learning, deep learning, computer vision, NLP, anomaly detection, time-series forecasting, predictive maintenance, and optimization.\nAI Platforms\nData ingestion, model development, feature pipelines, API/microservice architecture, model serving, GPU platforms, observability, and enterprise integration.\nMLOps / LLMOps\nCI/CD, experiment tracking, registries, evaluation, prompt/model lifecycle, monitoring, drift detection, feedback loops, and governance.\nEnterprise Architecture\nCloud, on-premises, hybrid and edge patterns; security; integration; scalability; resilience; cost optimization; and reusable reference architectures.\nRequired Technical Skills\nAI, GenAI & Agentic AI\nPython; PyTorch and/or TensorFlow; Hugging Face ecosystem\nLLMs, embeddings, RAG, multimodal and vision-language models\nAgent frameworks, workflow orchestration, tool/function calling, memory and evaluation\nFine-tuning, prompting, model evaluation, safety, guardrails and optimized inference\nCAD, Drawings & Engineering Data\nHands-on understanding of CAD/engineering drawing structures, layers, blocks, symbols, geometry and metadata\nExperience with OCR, document AI, computer vision and VLM-based extraction from complex technical drawings\nIntegration patterns for CAD/PLM/BIM/GIS and engineering document repositories\nFamiliarity with common formats such as DWG, DXF, DGN, IFC, STEP, IGES, PDF and raster images\nData, Cloud & Platforms\nOne or more of Azure, AWS or Google Cloud; Databricks or Microsoft Fabric is advantageous\nVector databases, graph databases/knowledge graphs, relational/NoSQL and time-series platforms\nDocker, Kubernetes, APIs, microservices, event-driven integration and GPU infrastructure\nHybrid, on-premises and edge deployment architectures\nMLOps, LLMOps & Governance\nCI/CD for AI, experiment tracking, model registry, prompt/model versioning and automated deployment\nQuality evaluation, telemetry, model monitoring, hallucination and drift detection, and feedback management\nResponsible AI, privacy, cybersecurity, access control, lineage, auditability and model risk governance\nExperience & Qualifications\n15+ years of overall experience in software, data, cloud, engineering systems, analytics, or AI-enabled transformation.\nAt least 5 years of significant AI/ML, advanced analytics, or intelligent automation experience, including architecture ownership.\nProven delivery of enterprise-scale AI/ML or Generative AI solutions in production.\nDemonstrated experience designing Agentic AI, RAG, multimodal, computer vision, or knowledge-graph solutions.\nExperience solving use cases involving CAD drawings, P&IDs, schematics, engineering documents, asset information, or industrial data is strongly preferred.\nStrong understanding of enterprise integration, APIs, data architecture, security, cloud and distributed systems.\nBachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, Data Science, or a related discipline.\nLeadership & Consulting Expectations\nTranslate ambiguous business and engineering challenges into a prioritized AI roadmap and defensible target architecture.\nCommunicate complex AI concepts clearly to engineering teams, business leaders, customers, security teams, and executive stakeholders.\nMake pragmatic build-versus-buy and model/platform decisions based on accuracy, scalability, latency, security, maintainability, and total cost.\nDrive architecture governance while enabling rapid experimentation and reusable accelerators.\nCoach cross-functional teams and promote disciplined engineering, documentation, evaluation, and responsible AI practices.\nPreferred Industry Exposure\nIndustrial manufacturing, energy, utilities, engineering and construction, automotive, aerospace, asset-intensive operations, digital twins, IoT, predictive maintenance, or engineering modernization.\nSuccess Measures\nAI solutions move from PoC to production with clear quality, safety, adoption, performance, and business-value metrics.\nCAD and engineering intelligence use cases reduce manual interpretation, improve retrieval and traceability, and accelerate engineering workflows.\nAgentic AI workflows demonstrate measurable task completion, groundedness, control, observability, and human accountability.\nReusable architectures, components, and governance patterns shorten delivery cycles across programs.\nPlatforms meet agreed standards for reliability, scalability, security, latency, and cost.\nIdeal Candidate Profile\nThe ideal candidate is an architect who can operate across the complete value chain: Business and engineering problem → data and drawings → model/LLM/VLM selection → Agentic AI and knowledge architecture → PoC → production deployment → MLOps/LLMOps → governance → measurable business value. 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