Staff Data Scientist, SMAI OI

We are seeking a highly motivated Agentic AI Data Scientist to lead the design and deployment of next-generation AI agents that drive end-to-end planning and operational optimization within SMAI (Smart Manufacturing & AI).

This role focuses on building goal-driven, multi-step AI systems (“agents”) that can autonomously plan, decide, and execute workflows across manufacturing planning, capacity optimization, and operations intelligence—unlocking cycle time reduction, capacity improvements, and decision automation at scale.

Key Responsibilities

1. Agentic AI Design & Development

• Design and develop agent-based AI systems capable of:

• Multi-step reasoning (planning + decision-making + execution)

• Autonomous orchestration across workflows and platforms

• Build multi-agent architectures for complex planning and operations use cases

• Develop agents that integrate:

• Optimization models (OR / mathematical programming)

• LLM-based reasoning and tool usage

• Ensure agents align with enterprise data, domain knowledge, and planning constraints

2. Planning & Operations Use Case Delivery

• Apply Agentic AI to key business problems such as:

• Capacity planning and capital investment optimization

• Production flow optimization and cycle time reduction

• Scenario simulation and decision support

• Translate business requirements into:

• Structured optimization problems

• AI-driven decision workflows

3. AI System Integration & Deployment

• Integrate agents into:

• Existing SMAI platforms and tools

• Data pipelines and enterprise systems

• Develop reusable frameworks for:

• Agent orchestration

• Knowledge retrieval (RAG / knowledge graph)

• Drive deployment strategy (embedded vs standalone agents depending on use case)

4. Cross-Functional Collaboration

• Partner with:

• Planning, Operations, and Manufacturing teams

• Data Engineering, MLOps, and Platform teams

• Translate domain knowledge into AI logic and workflows

• Communicate technical solutions to business stakeholders

Required Skillsets

Core AI / Software Engineering

• Strong programming skills in Python (preferred), plus familiarity with modern AI frameworks

• Experience with LLMs / GenAI ecosystems (e.g., agent frameworks, tool-use, orchestration)

• Solid understanding of:

• Prompt engineering

• Retrieval-Augmented Generation (RAG)

• Multi-agent systems

Optimization & Decision Science (Critical)

• Strong background in Operations Research / Optimization, including:

• Linear / Mixed Integer Programming

• Heuristics / metaheuristics

• Simulation models

• Experience translating real-world planning problems into mathematical models

Agentic AI & System Design

• Understanding of agentic AI principles:

• Goal-based and utility-based agents

• Planning + reasoning + execution loops

• Experience designing:

• Autonomous workflows

• Multi-step decision systems

• Tool-using AI agents

Data & Systems Integration

• Experience working with:

• Structured and unstructured data

• APIs and enterprise systems integration

• Familiarity with:

• Data pipelines (e.g., Spark, SQL)

• MLOps / deployment pipelines

Business & Domain Skills (Preferred)

• Experience in manufacturing, supply chain, or planning domains

• Strong problem-solving skills with ability to:

• Connect AI solutions to business value

• Quantify impact (capacity, cost, cycle time)

Minimum Qualifications

• Bachelor’s or Master’s degree in:

• Computer Science, Data Science, Industrial Engineering, Operations Research, or related field

• 3–5+ years of experience in:

• AI/ML engineering, or

• Optimization / decision science, or

• Advanced analytics in operations/planning

• Proven experience building production-grade AI or optimization solutions

Preferred Qualifications

• PhD in AI, Machine Learning, or Operations Research

• Experience with:

• Agent frameworks (LangChain, AutoGen, CrewAI, etc.)

• Reinforcement learning or adaptive systems

• Knowledge graphs and domain-specific AI tuning

• Experience in semiconductor or advanced manufacturing environments