Job Summary
To lead advanced data science initiatives, develop predictive models, and drive data-driven decision-making by extracting meaningful insights from complex datasets, enabling business growth and innovation.
We are building a high-impact engineering team to support multiple emerging Smart Manufacturing opportunities across semiconductor and advanced manufacturing customers.
This role is designed for adaptable AI/ML/GenAI engineers who can be seeded into high-visibility engagements spanning predictive maintenance, process intelligence, AI copilots, code generation platforms, and intelligent automation systems.
Opportunities will evolve dynamically based on customer needs, and selected candidates will work across industrial AI, GenAI, and data-driven transformation initiatives.
Key Responsibilities
Design, develop, and deploy AI/ML/GenAI solutions for smart manufacturing use cases.
Analyze structured and unstructured industrial data (sensor data, logs, quality reports, code repositories, engineering documentation).
Build predictive models (e.g., anomaly detection, forecasting, optimization, recommendation systems).
Develop LLM-powered applications using prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
Create intelligent copilots and domain-specific automation solutions.
Integrate AI systems with enterprise platforms (MES, PLM, ERP, Quality systems, Engineering tools).
Collaborate with customer stakeholders including manufacturing, quality, maintenance, and engineering teams.
Ensure scalability, reliability, explainability, and governance of AI solutions.
Participate in solution architecture discussions and PoC-to-production transitions.
Contribute to reusable frameworks and accelerators for future engagements.
Required Technical Skills
AI / ML / Data Science
Strong foundation in Machine Learning (supervised, unsupervised, time-series, anomaly detection).
Experience in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
Feature engineering and data preprocessing expertise.
Model evaluation and performance optimization.
GenAI / LLMs
Experience working with Large Language Models (OpenAI, Llama, Azure OpenAI, etc.).
Prompt engineering and evaluation frameworks.
RAG pipeline development.
Familiarity with vector databases (FAISS, Pinecone, Chroma, etc.).
Exposure to LLM fine-tuning and guardrail mechanisms.
Engineering & Deployment
API development (FastAPI/Flask).
Cloud platforms (Azure/AWS/GCP).
Containerization (Docker) and basic DevOps/MLOps practices.
Version control (Git) and CI/CD familiarity.
Key Responsibilities
2. Design and optimize algorithms for data processing, feature engineering, and pattern recognition.
3. Lead data exploration, mining, and visualization to uncover trends and actionable insights.
4. Collaborate with cross-functional teams to integrate data science solutions into business strategies.
5. Drive innovation by leveraging advanced statistical techniques, deep learning, and big data technologies.
6. Ensure data integrity, governance, and best practices in model development and deployment.
7. Mentor junior data scientists and promote a data-driven culture within the organization.
8. Stay ahead of industry trends and emerging technologies to enhance analytical capabilities.