Key skills
PythonJavaScalaMachine Learning FrameworksTensorFlowPyTorchScikit-learnData Processing ToolsPandasSparkHadoopREST APIsMicroservices ArchitectureCloud EnvironmentsAWSAzureGCPAlgorithmsData StructuresSoftware EngineeringDeep LearningNatural Language ProcessingComputer VisionMLOps ToolsMLflowKubeflowSageMakerContainerizationDockerOrchestrationKubernetesCI/CD PipelinesDevOps PracticesAIMachine LearningMLNLPGenerative AIscikit-learnMLOpsCI/CD
About this role
UST is a mission-driven company that transforms lives through technology. They are seeking a highly skilled AI/ML Engineer to design, develop, and deploy scalable machine learning models and AI-powered solutions for global clients across industries.
Responsibilities:
- Design, build, and deploy machine learning models and AI solutions in production environments
- Collaborate with data scientists, engineers, and business stakeholders to translate requirements into technical solutions
- Develop and optimize data pipelines for training, validation, and inference
- Apply best practices in model evaluation, monitoring, and lifecycle management (MLOps)
- Work with large-scale structured and unstructured datasets
- Integrate AI/ML models into enterprise applications and cloud platforms
- Continuously research and implement state-of-the-art AI techniques and tools
- Ensure scalability, performance, and reliability of deployed systems
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field
- 3+ years of experience in AI/ML engineering or related roles
- Strong programming skills in Python (preferred), Java, or Scala
- Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Experience with data processing tools such as Pandas, Spark, or Hadoop
- Familiarity with REST APIs and microservices architecture
- Experience deploying models in cloud environments (AWS, Azure, or GCP)
- Solid understanding of algorithms, data structures, and software engineering principles
- Experience with deep learning, NLP, or computer vision use cases
- Knowledge of MLOps tools such as MLflow, Kubeflow, or SageMaker
- Experience with containerization (Docker) and orchestration (Kubernetes)
- Familiarity with CI/CD pipelines and DevOps practices
- Exposure to generative AI, LLMs, or prompt engineering is a plus