Integration Architect

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 Jetson/IGX, x86 GPU, Intel iGPU/OpenVINO, 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.