AI Consultant – Manufacturing & SLM

Santa Clara County, CA - USA

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

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.

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