Position Summary:

The AI Engineer 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 ideal candidate combines hands-on technical depth across the full AI/ML lifecycle with strong collaboration skills, translating requirements from product and engineering leadership into scalable, reliable, production-grade solutions while upholding security, privacy, and responsible-AI practices.

Key Responsibilities:

Solution Development

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

Deployment & MLOps

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

Collaboration & Process

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

Compliance & Quality

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

Requirements:

Education

Bachelor’s degree in Computer Science, Software Engineering, Data Science, Data Engineering, Electrical/Computer Engineering, or related field required

Master’s degree preferred (AI/ML, Computer Science, Software Engineering, or related)

Experience

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

Technical Skills

Experience with LLM-based applications (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; ability to contribute to scalable, maintainable architectures and follow established engineering standards

Working knowledge of data security, privacy, and responsible AI practices (access control, governance, auditability)

Familiarity with software verification/validation, risk-based testing, and quality management practices in regulated or quality-managed environments
Demonstrated ability and willingness to use AI tools to improve productivity, decision-marking, work quality, and to reduce costs. The successful candidate must be able to identify appropriate AI use cases and critically evaluate AI-generated outputs.

Professional Skills

Able 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; able to collaborate effectively across engineering and cross-functional teams and contribute to technical documentation and design discussions

Strong prioritization and organizational skills; able to manage multiple tasks and deliver high-quality results

Preferred / Nice-to-Have

Relevant certifications: Azure AI Engineer, AWS ML Specialty, Databricks, or equivalent

Understanding of embedded ML applications

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