Role description
About the Role
We are seeking an AI Engineer to design, build, and deploy production-grade machine learning and artificial intelligence systems across our product ecosystem. In this role, you will bridge the gap between applied research and scalable software engineering to solve complex business problems. Your work will directly impact end-user experience by integrating high-performance models into real-time operational workflows.
Responsibilities
- Design, develop, and deploy scalable machine learning and deep learning models into production environments.
- Build robust data ingestion, feature engineering, and inference pipelines that handle high-throughput workloads.
- Optimize models for latency, compute efficiency, and memory footprint in cloud infrastructure.
- Implement monitoring, observability, and retraining pipelines to detect and mitigate data drift.
- Collaborate with backend engineers and product teams to integrate AI capabilities into customer-facing applications.
- Conduct rigorous evaluations, benchmark model performance, and document technical architectures.
Required Qualifications
- Bachelor or Master degree in Computer Science, Data Science, Mathematics, or a related technical field.
- At least 3 years of experience deploying machine learning or deep learning models to production.
- Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX.
- Practical experience with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker and Kubernetes.
- Solid understanding of software engineering fundamentals, including RESTful API design, version control, and CI/CD pipelines.
- Strong background in data structures, algorithms, and applied linear algebra or statistics.
Nice to Have
- Experience with LLM orchestration frameworks, retrieval-augmented generation architectures, and vector databases.
- Familiarity with MLOps platforms such as Kubeflow, MLflow, or Weights & Biases.
- Background in optimizing inference with tools like TensorRT, ONNX, or Triton Inference Server.
- Contributions to open-source machine learning projects or publications in peer-reviewed venues.
- Experience managing distributed training workloads using frameworks like Ray or DeepSpeed.
Requirements
- Minimum 4+ years of professional experience in software QA/testing, with substantial hands-on experience in automation testing
- Practical experience developing and maintaining automated test scripts for web applications
- Hands-on experience with at least one major automation framework, such as Selenium, Playwright, Cypress, or an equivalent tool
- Programming or scripting proficiency in Java, JavaScript/TypeScript, Python, or an equivalent language
