We are looking for an experienced AI Consultant with deep expertise in machine learning, deep learning, and generative AI, coupled with domain knowledge in Manufacturing and Service Lifecycle Management (SLM) — particularly in automotive (trucks, buses) and heavy equipment industries. The ideal candidate will be a full stack AI engineer capable of architecting, deploying, and scaling AI solutions across design, production, quality, aftersales, and service operations. This role blends hands-on technical development with consultative leadership, including pre-sales, solutioning, prototyping, and client enablement. Key Responsibilities 1. AI Solutioning & Consulting • Partner with manufacturing and service leaders to identify high-value AI use cases across product design, predictive maintenance, warranty analytics, service operations, and supply chain optimization. • Drive pre-sales engagements, client workshops, and AI opportunity assessments for industrial clients. • Develop proof-of-concepts, rapid prototypes, and demos to demonstrate business value. • Translate business problems into AI/ML solution architectures and roadmaps. 2. Technical Leadership • Design and build end-to-end AI pipelines for time-series analysis, anomaly detection, vision-based inspection, and document understanding. • Lead development of GenAI applications and agentic AI workflows for service manuals, parts lookup, and technician copilots. • Architect and deploy RAG-based knowledge assistants trained on technical documentation, service data, and IoT telemetry. • Work across data engineering, modeling, and deployment, ensuring full lifecycle delivery and performance optimization. 3. Cloud Engineering & MLOps • Deliver AI workloads on AWS (SageMaker, Bedrock), Azure (ML, OpenAI, AI Studio), or GCP (Vertex AI, Gemini). • Implement MLOps/LLMOps practices for model versioning, deployment automation, and monitoring. • Deploy containerized solutions with Docker/Kubernetes and expose models through APIs (FastAPI, Flask, or similar). • Integrate with edge AI or IoT platforms for predictive and real-time inference scenarios. 4. Domain Expertise – Manufacturing & Service Lifecycle • Apply AI across the end-to-end product and service lifecycle, including: • Product Design: Quality prediction, digital twins, defect classification. • Production: Process optimization, yield improvement, quality inspection using computer vision. • Aftermarket Services: Predictive maintenance, spare parts forecasting, intelligent service documentation. • Warranty & Field Data Analytics: Root cause analysis, failure mode detection, service call optimization. • Design GenAI copilots for service engineers and dealerships, integrating technical documentation, sensor data, and knowledge graphs. • Enable closed-loop feedback between engineering, manufacturing, and service through intelligent automation. 5. Thought Leadership & Enablement • Represent the organization in client solutioning sessions, RFPs, and innovation showcases. • Mentor teams in full stack AI development, industrial AI frameworks, and GenAI best practices. • Collaborate with domain and product experts to evolve AI-driven SLM accelerators and reference architectures. Required Skills & Qualifications • 12–15 years of experience in AI/ML, with at least 2+ years in Generative AI, LLMs, or Agentic AI. • Strong foundation in machine learning, deep learning, and industrial AI (vision, NLP, time series). • Expertise in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face, and LangChain. • Proven experience delivering solutions on AWS / Azure / GCP cloud environments. • Hands-on experience with containerization (Docker), orchestration (Kubernetes), and API deployment. • Familiarity with MLOps / LLMOps tools (MLflow, Azure ML, Vertex AI Pipelines, Kubeflow). • Strong understanding of manufacturing operations, IoT/edge AI, and service lifecycle data models. • Excellent communication and presentation skills for engaging technical and business stakeholders. Preferred Skills • Exposure to Digital Twin frameworks, predictive maintenance systems, and industrial IoT architectures. • Experience with vector databases (Pinecone, Weaviate, FAISS, Azure AI Search). • Knowledge of PLM, ERP, and SLM platforms (PTC Windchill, Siemens Teamcenter, SAP S/4HANA, etc.). • Background in automotive, commercial vehicles, or heavy equipment manufacturing. • Certification in Azure AI Engineer, AWS Machine Learning Specialty, or GCP Professional ML Engineer. Apply tot his job