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Have you got what it takes to succeed The following information should be read carefully by all candidates.
This role is ideal for engineers early in their AI/ML career who are solid coders and eager to work at the intersection of software engineering, applied ML, and enterprise AI platforms.
Key Responsibilities
Design, develop, test, and maintain production‑quality software supporting AI/ML solutions.
Implement and support AI‑enabled features using modern ML/LLM techniques, APIs, and services.
Contribute to Copilot‑enabled workflows (e.g., Teams, Outlook, Word, Excel) where applicable, including prompt refinement and agent‑based task automation.
Build and maintain backend services, APIs, and integrations that support AI/ML use cases.
Apply GenAI/LLM patterns: Build RAG pipelines, prompt management, evaluation harnesses, and safety mitigations. Integrate embeddings, vector stores, and caching strategies for latency/cost targets.
Participate in model integration and lifecycle activities: experimentation, evaluation, deployment, monitoring, and iteration. Instrument solutions for monitoring (quality, drift, bias, performance, cost).
Write unit tests, integration tests, and documentation to ensure reliability, security, and maintainability.
Collaborate closely with product managers, designers, and senior engineers to translate requirements into working solutions.
Follow enterprise standards for security, data handling, and governance when working with AI systems.
Required Qualifications
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
Strong programming background, especially in Python and/or at least one of Java, C#, or similar.
Solid understanding of software engineering fundamentals: data structures, APIs, version control, testing, and CI/CD.
Working knowledge of AI/ML fundamentals (machine learning concepts, LLMs, embeddings, evaluation basics) and tooling (scikit-learn, XGBoost, PyTorch/TensorFlow, Pandas/Spark).
Experience building APIs/services (REST/gRPC), containerization (Docker), and orchestration (Kubernetes).
Exposure to MLOps practices: CI/CD, MLflow/Kubeflow, model registries, automated testing.
Preferred Qualifications
Experience using or integrating Microsoft 365 Copilot, Copilot Agents, or similar enterprise GenAI tools.
Exposure to prompt engineering, agent‑based workflows, or AI‑assisted productivity tools.
Familiarity with cloud platforms (Azure preferred), Power Platform, or Microsoft Graph integrations.
Experience supporting AI features in production systems.
We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. xcfaprz Visit our FAQs for more information about requesting an accommodation.
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Sprachkenntnisse
- English
Hinweis für Nutzer
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