AI Engineer

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 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 solutionsDevelop 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