Purpose & Overall Relevance for the Organization
A Machine Learning Engineer applies foundational knowledge of the end-to-end Model Development Lifecycle (MDLC), software engineering, cloud technologies, and modern AI methodologies to help build, deploy, and scale machine learning solutions. They collaborate with cross-functional teams to transform proofs-of-concept into reliable and scalable production systems — with growing focus on Generative AI and agentic AI frameworks.
Key Responsibilities
Machine Learning Engineering
- Support the design and development of ML components for data and ML infrastructure (data pipelines, feature stores, model training/inference services)
- Assist in implementing end-to-end ML pipelines (MLOps), including data ingestion, feature engineering, training, deployment, and model monitoring
- Work with data scientists to productionize models and ensure business value is consistently delivered
- Contribute to model observability — logging, drift tracking, performance dashboards
GenAI & Agentic AI
- Use LLMs, prompt engineering, embeddings, and vector stores to enable intelligent applications
- Build small-scale AI agents using frameworks like LangChain, LlamaIndex, or equivalent
- Experiment with responsible and explainable use of foundation models to solve clear business problems
Analytics
- Assist in applying machine learning techniques with guidance from senior engineers or data scientists.Perform exploratory data analysis and support feature selection and data preparation
- Use unsupervised learning when appropriate for early insights or pattern discovery
Data Management & Engineering
- Support creation, improvement, and validation of curated datasets for ML applications
- Contribute to data quality checks, schema design, and efficient feature retrieval
- Follow best practices for security, accessibility, and ethical use of data.
Programming / Software Development
- Write clean, reliable, well-tested code (primarily in Python)
- Implement and maintain CI/CD workflows for ML components with supervision
- Deploy ML workloads on cloud or on-prem environments using modern tooling.
Visualization & Storytelling
- Build automated dashboards to support model/data health visibility
- Communicate insights clearly to technical and non-technical stakeholders.
Testing & Reliability
- Contribute to writing unit, integration, and regression tests for ML components
- Monitor test outcomes and support issue resolution.
Education & Experience — Minimum Qualifications
- 1+ years experience in a Machine Learning, Data Engineering, or AI-focused software engineering role (internships and academic projects count)
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (Master’s not required)
- Solid understanding of Python, data structures, and basic software engineering practices
- Familiarity with:
- ML frameworks: scikit-learn, TensorFlow, or PyTorch
- GenAI / Agentic frameworks: LangChain, LlamaIndex, Hugging Face, vector databases (e.g., FAISS, Pinecone)
- MLOps concepts: model packaging, CI/CD, containerization (Docker), REST/Batch inference
- Some exposure (academic or project-based) to cloud platforms (AWS, Azure, GCP) and distributed data tools (Spark, Kafka) is a plus
- Interest in modern AI topics such as prompt engineering, embeddings, and responsible AI.
Soft Skills
- Clear and concise verbal and written communication (English)
- Collaborative mindset and willingness to learn from peers
- Ability to break down complex problems and take initiative on tasks
- Resilient, detail-oriented, and passionate about emerging AI technologies.
adidas celebrates diversity, supports inclusiveness and encourages individual expression in our workplace. We do not tolerate the harassment or discrimination toward any of our applicants or employees. We are an equal opportunity employer.