AI Consultant

Location: Santa Clara

Experience: Years

Must Have:

• 12–15 years of experience in AI/ML, with at least 2+ years in Generative AI, LLMs, or Agentic AI.
• The candidate should be directly managing software engineering teams to implement AI-driven SDLC improvements.
• 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

Job Description:

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.

Why Join Us

• Drive the next wave of AI-led digital transformation in manufacturing and aftersales service.
• Build intelligent copilots, autonomous agents, and predictive systems for leading global OEMs.
• Collaborate with a team of AI experts and domain consultants pushing the frontier of industrial and agentic AI.
• Influence the evolution of service lifecycle management through data-driven intelligence and automation.

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