We are seeking a motivated AI/ML Engineer (2–3 years of relevant experience) to contribute to the design, development, and scaling of AI/ML solutions on our analytics platform serving manufacturing and semiconductor sectors. This role blends classical machine learning with Generative AI to support applications such as anomaly detection, predictive maintenance, market intelligence, test plan generation, and customer support tools.

You will work on end-to-end tasks involving numerical sensor/test data, unstructured text, and LLM-powered workflows in a regulated industrial environment that values precision, reliability, and risk mitigation. This is a hands-on role requiring solid implementation skills and growing architectural understanding.

Key Responsibilities

Contribute to unified AI/ML capabilities by integrating classical ML models with Generative AI platforms (primarily AWS Bedrock) for applications in semiconductor manufacturing and risk analytics.
Support the development of anomaly detection and predictive maintenance systems using classical ML (XGBoost, Scikit-learn) on sensor and test data, including basic drift detection and model monitoring.
Assist in building and maintaining RAG pipelines and agentic workflows for tasks like automated manufacturing test plan generation, with focus on accuracy and hallucination reduction.
Help develop summarization and information extraction pipelines that process news articles, press releases, and open-source data into actionable market intelligence reports (using chunking, embeddings, semantic search, map-reduce patterns, etc.).
Contribute to a customer-facing GenAI Q&A chatbot delivering domain-specific insights based on sensor data and test plans.
Work on classical ML problems (regression, classification, clustering, time-series) and explore hybrid integrations with GenAI where beneficial.
Apply NLP techniques (RNNs/LSTMs and LLM-based methods) to extract insights from unstructured sources such as reports, logs, and pricing data.
Collaborate with MLOps, data engineering, domain experts, and product teams in an Agile/Scrum environment to build, validate, and deploy models with CI/CD, observability, and testing.
Perform model evaluation, hyperparameter tuning, feature engineering, and basic bias/risk assessment; support monitoring for data/concept drift.
Help enhance data pipelines using tools like Apache Spark, vector databases, and distributed processing.
Stay current with classical ML, RAG, agentic systems, and industrial analytics; suggest improvements that deliver business value.

Qualifications

Requirements

Must-have qualifications

Bachelor's or Master's degree in Machine Learning, Computer Science, Data Science, Statistics, or a closely related quantitative field.
2–3 years of professional experience as a Machine Learning Engineer / AI Engineer with demonstrated contributions to both classical ML and GenAI/LLM-based projects.
Hands-on experience with classical ML frameworks (Scikit-learn, XGBoost) and deep learning/NLP tools (TensorFlow or PyTorch, basic RNNs/LSTMs).
Practical exposure to RAG architectures, prompt engineering, vector databases (embeddings, similarity search), and agentic tools (LangChain/LangGraph, CrewAI, Bedrock Agents, or similar).
Experience building or supporting summarization/extraction pipelines for document sets and classical ML models for anomaly detection or predictive tasks on numerical/time-series data.
Solid Python skills, version control (Git), testing, and familiarity with MLOps practices (model monitoring, basic CI/CD).
Working knowledge of AWS Bedrock (Knowledge Bases, Agents) or comparable GenAI platforms.
Comfort working in Agile/Scrum teams and applying standard ML evaluation practices.
Fluency in English, including technical communication.

Strongly preferred

Exposure to manufacturing, semiconductors, sensor analytics, test/measurement instrumentation, or industrial risk domains.
Experience with Apache Spark or large-scale data processing.
Prior work combining classical ML with GenAI (e.g., using ML for filtering/reranking in RAG).
Portfolio or projects showcasing production-oriented classical ML + GenAI solutions. Familiarity with the Model Context Protocol (MCP) or standardized LLM/tool integrations.