Job Description

Location: Santa Clara

Experience: 10 - 21 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

:

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

.