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Software Engineer III
| Company | Walmart |
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| Location | Bentonville, AR, USA |
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| Salary | $90000 – $180000 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Mid Level, Senior |
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Requirements
- Experience in ML systems engineering (esp. time series and sequence models) and full stack software development.
- Proven ability in backend engineering (Python, FastAPI, Flask, Node.js, or similar), API design, and microservice architecture.
- Strong grasp of cloud platforms (GCP, Azure, or AWS), containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform, CloudFormation).
- Experience implementing CI/CD, DevOps best practices, monitoring, and logging for both ML and web services.
- Experienced in distributed computing and processing big data for feature engineering, model training, and inference.
- Hands-on with PyTorch, TensorFlow/Keras, Scikit-learn, and MLOps tools (MLflow, Vertex AI, Sagemaker, etc.).
Responsibilities
- Develop, and deploy scalable, production-grade time series forecasting systems using advanced global models to support high-volume retail and e-commerce workloads.
- Build and orchestrate end-to-end ML pipelines for data ingestion, training, evaluation, deployment, and monitoring, leveraging workflow managers such as Airflow, Astronomer, or Kubeflow.
- Engineer robust backend services and RESTful APIs to serve ML models and analytics, ensuring secure, reliable, and low-latency data access for internal and external stakeholders.
- Implement and automate CI/CD pipelines for code, models, and infrastructure, applying best practices for version control, testing, code review, and continuous integration.
- Integrate generative AI and NLP-based analytics assistants into both backend and frontend workflows for intelligent querying, reporting, and data synthesis.
- Utilize distributed compute frameworks (Spark, Ray, Dask) for large-scale data processing and model training, ensuring scalable and efficient resource use.
- Develop and maintain cloud-native microservices and infrastructure (Docker, Kubernetes, Terraform, GCP, Azure) for robust deployment, scaling, and monitoring of ML and web applications.
- Collaborate closely with cross-functional teams (software engineers, data scientists, product, business) to gather requirements, design end-to-end solutions, and deliver value through technology.
Preferred Qualifications
- Experience with retrieval-augmented generation (RAG), LLM fine-tuning, and large-scale NLP pipelines.
- Expertise in real-time data processing, WebSockets, and serverless architectures.
- Domain experience in retail, e-commerce, or financial analytics.
