AI Engineer

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 solutionsDevelop and integrate APIs, services, and data pipelines that support AI functionalityContribute to identifying and prototyping high-value AI use cases in partnership with business, engineering, and lab-services stakeholdersTranslate defined requirements from product managers and engineering leadership into working solutionsDevelop reliable, maintainable, and scalable software following team standards and established architectural guidelinesDeployment & MLOps Support the deployment and maintenance of AI/ML applications in production environmentsImplement and maintain monitoring, evaluation, and logging mechanisms for AI systemsContribute to CI/CD pipelines and model/application lifecycle managementSupport debugging, performance optimization, and system reliabilityCollaboration & ProcessWork closely with the software engineering team, product stakeholders, and cross-functional partnersCollaborate with the Software Engineering Manager and team on system design and implementation approachesParticipate in sprint planning, code reviews, and agile development processesCommunicate progress, risks, and technical trade-offs clearly to the Software Engineering Manager and project stakeholdersCompliance & QualityFollow established standards for security, privacy, and responsible AI usageSupport software validation, testing, and documentation per company processes (QMS where applicable)Ensure compliance with internal development and deployment policiesPerform other duties as assignedRequirements:EducationBachelor’s degree in Computer Science, Software Engineering, Data Science, Data Engineering, Electrical/Computer Engineering, or related field requiredMaster’s degree preferred (AI/ML, Computer Science, Software Engineering, or related)Experience2+ 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 environmentsHands-on capability designing and building AI-powered applications, including rapid prototyping through production deploymentPractical experience with the AI/ML lifecycle: data pipelines, evaluation, deployment, monitoring, maintenance, and MLOpsTechnical SkillsExperience 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 practicesStrong software design and modular development skills; ability to contribute to scalable, maintainable architectures and follow established engineering standardsWorking 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 environmentsDemonstrated 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 SkillsAble to complete assigned work with appropriate guidance while contributing effectively as a team player in a fast-paced, deadline-driven environmentStrong interpersonal and communication skills; able to collaborate effectively across engineering and cross-functional teams and contribute to technical documentation and design discussionsStrong prioritization and organizational skills; able to manage multiple tasks and deliver high-quality resultsPreferred / Nice-to-HaveRelevant certifications: Azure AI Engineer, AWS ML Specialty, Databricks, or equivalentUnderstanding of embedded ML applications