The AI/ML Associate Engineer is a hands‑on software engineer who applies strong programming fundamentals to build, integrate, and support AI/ML solutions. The role focuses on writing production‑quality code while contributing to AI‑powered features, including (where applicable) solutions that leverage Microsoft 365 Copilot and Copilot Agents.
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.