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