AI Engineer

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