- Company Name
- Mogi I/O : OTT/Podcast/Short Video Apps for you
- Job Title
- AI Innovation Lead – Vision, NLP & Agent Workflows
- Job Description
-
**Job Title**
AI Innovation Lead – Vision, NLP & Agent Workflows
**Role Summary**
Spearhead the design, architecture, and deployment of AI solutions for manufacturing and service lifecycle management across automotive, commercial vehicles, heavy equipment, and industrial sectors. Blend full‑stack AI engineering with consultative leadership: workshops, pre‑sales support, PoC development, and end‑to‑end solution delivery on cloud platforms (AWS, Azure, GCP).
**Expectations**
- 10–21 years of progressive AI/ML experience, 12–15 years in AI/ML with 2+ years focused on generative AI, LLMs, and agentic AI.
- Proven track record of building production‑grade AI pipelines, MLOps, and cloud‑native deployments.
- Deep knowledge of manufacturing operations, IoT, and SLM data models.
- Strong client‑facing communication and leadership skills.
**Key Responsibilities**
- Identify and articulate AI use cases; lead workshops & client engagements.
- Design, prototype, and validate PoCs demonstrating business value.
- Build end‑to‑end pipelines for vision, NLP, time‑series, and generative AI applications.
- Implement agentic workflows, RAG knowledge systems, and predictive maintenance models.
- Deploy models via Docker/Kubernetes, APIs, and managed cloud services.
- Establish MLOps pipelines (CI/CD, monitoring, logging) using MLflow, Kubeflow, Azure ML, Vertex Pipelines, etc.
- Translate data into actionable insights for design, production, quality, and service operations.
- Mentor cross‑functional teams and contribute reusable AI frameworks.
**Required Skills**
- Languages & Frameworks: Python, TensorFlow, PyTorch, Scikit‑learn, Hugging Face, LangChain.
- Cloud Platforms: AWS, Azure, GCP – with experience in ML services and model deployment.
- Containers & Orchestration: Docker, Kubernetes, API deployment.
- MLOps/LLMOps tools: MLflow, Azure ML, Vertex Pipelines, Kubeflow.
- AI Domains: Computer Vision, NLP, Time‑Series Analysis, Generative AI, RAG, Agentic AI.
- Domain Knowledge: Manufacturing operations, IoT, SLM data models, predictive maintenance.
- Communication: High‑level client presentations, workshops, and stakeholder management.
**Required Education & Certifications**
- Bachelor’s (or higher) in Computer Science, Engineering, Data Science, or related field.
- Cloud AI certifications preferred (AWS ML Specialty, Azure AI Engineer, GCP ML Engineer).