AI Consultant – Manufacturing Domain
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
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