Data Scientist

Ametek, Inc.•Mount Prospect, IL

35d

About The Position

The AI Engineer/Data Scientist designs, develops, and deploys AI/ML-powered software solutions that advance Atlas's business operations and product innovation. Working within the software engineering team, this role builds and integrates AI capabilities—including LLM-based tools, copilots, data pipelines, and cloud applications—in alignment with established architecture, engineering standards, and product roadmaps. The role calls for hands-on technical depth across the full AI/ML lifecycle and strong collaboration skills to translate requirements from product and engineering leadership into scalable, reliable, production-grade solutions while upholding security, privacy, and responsible-AI practices.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Data Engineering, Electrical/Computer Engineering, or a related field.
2+ years in software engineering, architecture, or applied data/ML engineering roles, with experience delivering AI/ML solutions (model development and/or LLM/RAG applications) into production environments.
Hands-on capability designing and building AI-powered applications, including rapid prototyping through production deployment.
Practical experience with the AI/ML lifecycle: data pipelines, evaluation, deployment, monitoring, maintenance, and MLOps.
Experience with LLM-based applications, including prompting patterns, tool/function calling, RAG, embeddings/vector databases, guardrails, and evaluation techniques.
Experience with cloud platforms (Azure or AWS), APIs/microservices, CI/CD pipelines, and secure deployment practices.
Strong software design and modular development skills, with the ability to contribute to scalable, maintainable architectures and follow established engineering standards.
Working knowledge of data security, privacy, and responsible AI practices, including access control, governance, and auditability.
Familiarity with software verification/validation, risk-based testing, and quality management practices in regulated or quality-managed environments.
Ability and willingness to use AI tools to improve productivity, decision-making, work quality, and reduce costs; ability to identify appropriate AI use cases and critically evaluate AI-generated outputs.
Ability to complete assigned work with appropriate guidance while contributing effectively as a team player in a fast-paced, deadline-driven environment.
Strong interpersonal and communication skills, including the ability to collaborate across engineering and cross-functional teams and contribute to technical documentation and design discussions.
Strong prioritization and organizational skills, with the ability to manage multiple tasks and deliver high-quality results.

Nice To Haves

Master’s degree in AI/ML, Computer Science, Software Engineering, or a related field.
Relevant certifications such as Azure AI Engineer, AWS ML Specialty, Databricks, or equivalent.
Understanding of embedded ML applications.

Responsibilities

Develop, test, and deploy AI-powered software applications, including LLM-based tools, copilots, and automation solutions.
Develop and integrate APIs, services, and data pipelines that support AI functionality.
Contribute to identifying and prototyping high-value AI use cases in partnership with business, engineering, and lab-services stakeholders.
Translate defined requirements from product managers and engineering leadership into working solutions.
Develop reliable, maintainable, and scalable software following team standards and established architectural guidelines.
Support the deployment and maintenance of AI/ML applications in production environments.
Implement and maintain monitoring, evaluation, and logging mechanisms for AI systems.
Contribute to CI/CD pipelines and model/application lifecycle management.
Support debugging, performance optimization, and system reliability.
Work closely with the software engineering team, product stakeholders, and cross-functional partners.
Collaborate with the Software Engineering Manager and team on system design and implementation approaches.
Participate in sprint planning, code reviews, and agile development processes.
Communicate progress, risks, and technical trade-offs clearly to the Software Engineering Manager and project stakeholders.
Follow established standards for security, privacy, and responsible AI usage.
Support software validation, testing, and documentation per company processes (QMS where applicable).
Ensure compliance with internal development and deployment policies.
Perform other duties as assigned.