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The role involves architecting integration solutions for supply chain systems, focusing on standardization and supporting integration programs while mentoring teams and driving AI integration.
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As an Integration Architect, you'll develop and lead enterprise integration strategies, translating complex requirements into scalable solutions while engaging stakeholders and overseeing implementation quality across projects.
Top Skills:
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The SIEM Integration Architect leads the integration and management of SIEM solutions, configures Cribl pipelines, designs data normalization strategies, and develops automation playbooks to enhance security monitoring and response in Microsoft Sentinel.
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Project Role : Integration Architect
Project Role Description : Architect an end-to-end integration solution. Drive client discussions to define the integration requirements and translate the business requirements to the technology solution. Activities include mapping business processes to support applications, defining the data entities, selecting integration technology components and patterns, and designing the integration architecture.
Must have skills : AI Agents & Workflow Integration
Good to have skills : NA
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: We are seeking a C-suite - facing Industrial AI & Agentic Systems Lead to architect, govern, and scale AI solutions - including AI, multi-agent, LLM-driven, tool-using autonomous systems - across manufacturing, supply chain, and plant operations. You will define the strategy-to-scale journey from high-value use case selection (OEE, yield, PdM, energy, scheduling, autonomous quality) to edge - cloud architectures, MLOps/LLMOps, Responsible & Safe AI /Agentic AI, and IT/OT convergence, delivering hard business outcomes. Roles & Responsibilities: 1.Strategy & C-Suite Advisory: -Define an Industrial AI + Agentic AI strategy & roadmap tied to OEE, yield, cost, throughput, energy, and sustainability KPI - with ROI/payback models. -Shape operating models (central CoE vs. federated), governance, funding, and product-platform scaling approaches. -Educate CxO stakeholders on where Agentic AI adds leverage (closed-loop optimization, autonomous workflows, human-in-the-loop decisioning). 2.Architecture & Platforms: - Design edge- plant - cloud reference architectures for ML + Agentic AI: data ingestion (OPC UA, MQTT, Kafka), vector DB/RAG layers, model registries, policy engines, observability, and safe tool execution - Define LLMOps patterns for prompt/version management, agent planning/execution traces, tool catalogs, guardrails, and evaluation harnesses. 3.Agentic AI (Dedicated): - Architect multi-agent systems (planner- solver - critic patterns) for: SOP generation & validation, root-cause analysis & corrective action recommendation, autonomous scheduling & rescheduling, MRO/work order intelligence, control room copilots orchestrating OT/IT tools. - Design tooling & action interfaces (function calling / tools registry) to safely let agents interact with MES/ERP/CMMS/SCADA/DCS, simulations (DES, digital twins), and optimization solvers (cuOpt, Gurobi, CP-SAT). - Establish policy, safety, and constraints frameworks (role-based agent scopes, allow/deny tool lists, human-in-the-loop gates, audit trails). -Implement RAG + knowledge graph + vector DB stacks for engineering/service manuals, logs, SOPs, and quality records to power grounded agent reasoning. -Set up evaluation & red-teaming for agent behaviors: hallucination tests, unsafe action prevention, KPI-driven performance scoring. 4.Use Cases & Solutions (Manufacturing Focus): - Computer Vision / Autonomous Quality (TAO, Triton, TensorRT) with agentic triage & escalation to quality engineers. - Predictive/Prescriptive Maintenance with agents orchestrating data retrieval, work order creation, spare part planning. - Process & Yield Optimization where agents run DOE, query historians, simulate scenarios (digital twins), recommend set-point changes. - Scheduling / Throughput Optimization with planner - optimizer agents calling OR/RL solvers. - GenAI/LLM for Manufacturing: copilots & autonomous agents for SOPs, RCA documentation, PLC/SCADA code refactoring (with strict guardrails). 5.MLOps, LLMOps, Edge AI & Runtime Ops: - Stand up MLOps + LLMOps: CI/CD for models & prompts, drift detection, lineage, experiment & agent run tracking, safe rollback. - Architect Edge AI on NVIDIA Jetson/IGX, x86 GPU, Intel iGPU/OpenVINO, ensuring deterministic latency, TSN/real-time where needed. - Implement observability for agents (traces, actions, rewards/scores, SLA adherence). 6.Responsible / Safe AI, Compliance & Security: - Codify Responsible AI and Agentic Safety policies: transparency, explainability (XAI), auditability, IP protection, privacy, toxicity & jailbreak prevention. - Align with regulations (e.g., GxP, FDA 21 CFR Part 11, ISO 27001, IEC 62443, ISO 26262, AS9100) for industrial domains. 7.Delivery, GTM & Thought Leadership: - Serve as chief architect / design authority on large AI + Agentic programs; mentor architects, data scientists/engineers, and MLOps/LLMOps teams. - Lead pre-sales, solution shaping, executive storytelling, and ecosystem partnership building (NVIDIA, hyperscalers, MES/SCADA, optimization, cybersecurity). Professional & Technical Skills: Must have Skills: - Proven AI at scale delivery record in manufacturing with quantified value and hands-on leadership of LLM/Agentic AI initiatives. - Deep understanding of shop-floor tech (MES/MOM, SCADA/DCS, historians - PI/AVEVA, PLC/RTUs), protocols (OPC UA, MQTT, Modbus, Kafka). - Expertise in ML & CV stacks (PyTorch/TensorFlow, Triton, TensorRT, TAO Toolkit) and LLM/Agentic stacks (function calling, RAG, vector DBs, prompt/agent orchestration). - MLOps & LLMOps (MLflow, Kubeflow, SageMaker/Vertex, Databricks, Feast, LangSmith/Evaluation frameworks, guardrails). - Edge AI deployment on NVIDIA/Intel/x86 GPUs, with K8s/K3s, Docker, Triton Inference Server. - Strong security & governance for IT/OT and AI/LLM (IEC 62443, Zero Trust, data residency, key/token vaults, prompt security). - Executive communication: convert complex AI+Agentic architectures into board-level impact narratives Good to have skills: - Agentic frameworks: LangGraph, AutoGen, CrewAI, Semantic Kernel, Guardrails, LMQL. - Optimization & RL: cuOpt, Gurobi, OR-Tools, RLlib, Stable Baselines. - Digital Twins & Simulation: NVIDIA Omniverse/Isaac/Modulus, AnyLogic, AspenTech, Siemens. - Knowledge graphs & semantics: Neo4j, RDF/OWL, SPARQL, ontologies for manufacturing. - Standards & frameworks: ISA-95, RAMI 4.0, MIMOSA, ISO 8000, DAMA-DMBOK. - Experience in regulated sectors (Pharma/MedTech, Aero/Defense, Automotive). - AI/ML/LLM: PyTorch, TensorFlow, ONNX, Triton, TensorRT, TAO Toolkit, RAPIDS,LangChain/LangGraph, AutoGen, Semantic Kernel, Guardrails, OpenVINO. - MLOps/LLMOps/DataOps: MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, Feast, Airflow/Prefect, Great Expectations, LangSmith, PromptLayer. - Edge/OT: NVIDIA Jetson/IGX, K3s/K8s, Docker, OPC UA, MQTT, Ignition, PI/AVEVA, ThingWorx. - Data/Streaming/RAG: Kafka, Flink/Spark, Delta/Iceberg/Hudi, Snowflake/BigQuery/Synapse - Vector DBs (Milvus, FAISS, Qdrant, Weaviate), KG (Neo4j). - Cloud : AWS/Azure/GCP(at least one at expert level), Kubernetes, Security (CISSP/IEC 62443) a plus. - Lean/Six Sigma/TPM nice to have credibility with operations. - Leadership & Behavioral Competencies: C-suite advisor & storyteller with outcome-first mindset. - Architectural authority balancing speed, safety, and scale. - People build across DS/ML, DE, MLOps/LLMOps, and OT. - Change leader who can operationalize AI & agents on real shop floors. Additional Info: - A minimum of 20 years of progressive information technology experience is required. - A Bachelors/master's in engineering / CS / Data Science (PhD preferred for R&D-heavy roles) is required. - This position is based at Bengaluru location.15 years full time education
Project Role Description : Architect an end-to-end integration solution. Drive client discussions to define the integration requirements and translate the business requirements to the technology solution. Activities include mapping business processes to support applications, defining the data entities, selecting integration technology components and patterns, and designing the integration architecture.
Must have skills : AI Agents & Workflow Integration
Good to have skills : NA
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: We are seeking a C-suite - facing Industrial AI & Agentic Systems Lead to architect, govern, and scale AI solutions - including AI, multi-agent, LLM-driven, tool-using autonomous systems - across manufacturing, supply chain, and plant operations. You will define the strategy-to-scale journey from high-value use case selection (OEE, yield, PdM, energy, scheduling, autonomous quality) to edge - cloud architectures, MLOps/LLMOps, Responsible & Safe AI /Agentic AI, and IT/OT convergence, delivering hard business outcomes. Roles & Responsibilities: 1.Strategy & C-Suite Advisory: -Define an Industrial AI + Agentic AI strategy & roadmap tied to OEE, yield, cost, throughput, energy, and sustainability KPI - with ROI/payback models. -Shape operating models (central CoE vs. federated), governance, funding, and product-platform scaling approaches. -Educate CxO stakeholders on where Agentic AI adds leverage (closed-loop optimization, autonomous workflows, human-in-the-loop decisioning). 2.Architecture & Platforms: - Design edge- plant - cloud reference architectures for ML + Agentic AI: data ingestion (OPC UA, MQTT, Kafka), vector DB/RAG layers, model registries, policy engines, observability, and safe tool execution - Define LLMOps patterns for prompt/version management, agent planning/execution traces, tool catalogs, guardrails, and evaluation harnesses. 3.Agentic AI (Dedicated): - Architect multi-agent systems (planner- solver - critic patterns) for: SOP generation & validation, root-cause analysis & corrective action recommendation, autonomous scheduling & rescheduling, MRO/work order intelligence, control room copilots orchestrating OT/IT tools. - Design tooling & action interfaces (function calling / tools registry) to safely let agents interact with MES/ERP/CMMS/SCADA/DCS, simulations (DES, digital twins), and optimization solvers (cuOpt, Gurobi, CP-SAT). - Establish policy, safety, and constraints frameworks (role-based agent scopes, allow/deny tool lists, human-in-the-loop gates, audit trails). -Implement RAG + knowledge graph + vector DB stacks for engineering/service manuals, logs, SOPs, and quality records to power grounded agent reasoning. -Set up evaluation & red-teaming for agent behaviors: hallucination tests, unsafe action prevention, KPI-driven performance scoring. 4.Use Cases & Solutions (Manufacturing Focus): - Computer Vision / Autonomous Quality (TAO, Triton, TensorRT) with agentic triage & escalation to quality engineers. - Predictive/Prescriptive Maintenance with agents orchestrating data retrieval, work order creation, spare part planning. - Process & Yield Optimization where agents run DOE, query historians, simulate scenarios (digital twins), recommend set-point changes. - Scheduling / Throughput Optimization with planner - optimizer agents calling OR/RL solvers. - GenAI/LLM for Manufacturing: copilots & autonomous agents for SOPs, RCA documentation, PLC/SCADA code refactoring (with strict guardrails). 5.MLOps, LLMOps, Edge AI & Runtime Ops: - Stand up MLOps + LLMOps: CI/CD for models & prompts, drift detection, lineage, experiment & agent run tracking, safe rollback. - Architect Edge AI on NVIDIA Jetson/IGX, x86 GPU, Intel iGPU/OpenVINO, ensuring deterministic latency, TSN/real-time where needed. - Implement observability for agents (traces, actions, rewards/scores, SLA adherence). 6.Responsible / Safe AI, Compliance & Security: - Codify Responsible AI and Agentic Safety policies: transparency, explainability (XAI), auditability, IP protection, privacy, toxicity & jailbreak prevention. - Align with regulations (e.g., GxP, FDA 21 CFR Part 11, ISO 27001, IEC 62443, ISO 26262, AS9100) for industrial domains. 7.Delivery, GTM & Thought Leadership: - Serve as chief architect / design authority on large AI + Agentic programs; mentor architects, data scientists/engineers, and MLOps/LLMOps teams. - Lead pre-sales, solution shaping, executive storytelling, and ecosystem partnership building (NVIDIA, hyperscalers, MES/SCADA, optimization, cybersecurity). Professional & Technical Skills: Must have Skills: - Proven AI at scale delivery record in manufacturing with quantified value and hands-on leadership of LLM/Agentic AI initiatives. - Deep understanding of shop-floor tech (MES/MOM, SCADA/DCS, historians - PI/AVEVA, PLC/RTUs), protocols (OPC UA, MQTT, Modbus, Kafka). - Expertise in ML & CV stacks (PyTorch/TensorFlow, Triton, TensorRT, TAO Toolkit) and LLM/Agentic stacks (function calling, RAG, vector DBs, prompt/agent orchestration). - MLOps & LLMOps (MLflow, Kubeflow, SageMaker/Vertex, Databricks, Feast, LangSmith/Evaluation frameworks, guardrails). - Edge AI deployment on NVIDIA/Intel/x86 GPUs, with K8s/K3s, Docker, Triton Inference Server. - Strong security & governance for IT/OT and AI/LLM (IEC 62443, Zero Trust, data residency, key/token vaults, prompt security). - Executive communication: convert complex AI+Agentic architectures into board-level impact narratives Good to have skills: - Agentic frameworks: LangGraph, AutoGen, CrewAI, Semantic Kernel, Guardrails, LMQL. - Optimization & RL: cuOpt, Gurobi, OR-Tools, RLlib, Stable Baselines. - Digital Twins & Simulation: NVIDIA Omniverse/Isaac/Modulus, AnyLogic, AspenTech, Siemens. - Knowledge graphs & semantics: Neo4j, RDF/OWL, SPARQL, ontologies for manufacturing. - Standards & frameworks: ISA-95, RAMI 4.0, MIMOSA, ISO 8000, DAMA-DMBOK. - Experience in regulated sectors (Pharma/MedTech, Aero/Defense, Automotive). - AI/ML/LLM: PyTorch, TensorFlow, ONNX, Triton, TensorRT, TAO Toolkit, RAPIDS,LangChain/LangGraph, AutoGen, Semantic Kernel, Guardrails, OpenVINO. - MLOps/LLMOps/DataOps: MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, Feast, Airflow/Prefect, Great Expectations, LangSmith, PromptLayer. - Edge/OT: NVIDIA Jetson/IGX, K3s/K8s, Docker, OPC UA, MQTT, Ignition, PI/AVEVA, ThingWorx. - Data/Streaming/RAG: Kafka, Flink/Spark, Delta/Iceberg/Hudi, Snowflake/BigQuery/Synapse - Vector DBs (Milvus, FAISS, Qdrant, Weaviate), KG (Neo4j). - Cloud : AWS/Azure/GCP(at least one at expert level), Kubernetes, Security (CISSP/IEC 62443) a plus. - Lean/Six Sigma/TPM nice to have credibility with operations. - Leadership & Behavioral Competencies: C-suite advisor & storyteller with outcome-first mindset. - Architectural authority balancing speed, safety, and scale. - People build across DS/ML, DE, MLOps/LLMOps, and OT. - Change leader who can operationalize AI & agents on real shop floors. Additional Info: - A minimum of 20 years of progressive information technology experience is required. - A Bachelors/master's in engineering / CS / Data Science (PhD preferred for R&D-heavy roles) is required. - This position is based at Bengaluru location.15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Accenture Bengaluru, Karnataka, IND Office
SEZ, Divyashree Techno Park 36/2, K R Puram, Hobli, Kundalahalli, Whitefield, Bengaluru, Karnataka, India, 560066
What you need to know about the Bengaluru Tech Scene
Dubbed the "Silicon Valley of India," Bengaluru has emerged as the nation's leading hub for information technology and a go-to destination for startups. Home to tech giants like ISRO, Infosys, Wipro and HAL, the city attracts and cultivates a rich pool of tech talent, supported by numerous educational and research institutions including the Indian Institute of Science, Bangalore Institute of Technology, and the International Institute of Information Technology.
