AI/ML Developer
Two95 International Inc.
Posted: March 11, 2026
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Quick Summary
Our company is looking for an experienced AI/ML Engineer to work on scalable, secure, and cost-optimized machine learning solutions on Google Cloud Platform using TensorFlow, PyTorch, and scikit. The ideal candidate will be responsible for designing and building AI programs, machine learning models, and applications that leverage these technologies. The successful candidate will have a strong background in programming languages such as Python, Java, and Scala, and experience with frameworks like TensorFlow and PyTorch.
Required Skills
Job Description
We are looking for AI/ML Engineer with 8 to 10 years of experience.
Job Summary:
• Work with business stakeholders, data scientists, and product managers to understand challenges and define clear requirements for AI-driven solutions.
• Architect and design scalable, secure, and cost-optimized machine learning solutions on GCP using a wide range of services, especially within Vertex AI.
• Build and code the core AI programs, machine learning models, and the applications that leverage them. Use languages like Python, Java, or Scala, along with frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Utilize generative AI, large language models (LLMs), and retrieval-augmented generation (RAG) to build features like chatbots or content summarization.
• Create pipelines for ingesting, cleaning, and transforming raw data into a format suitable for training ML models. This often involves services like Dataflow, Dataproc, and BigQuery.
• Use Vertex AI Pipelines to automate and orchestrate the end-to-end machine learning lifecycle, making the processes repeatable and auditable.
• Implement MLOps practices for continuous integration/continuous delivery (CI/CD) of machine learning models. Automate deployment, versioning, and testing processes using tools like Cloud Build and Vertex AI Model Registry.
• Store model artifacts in Cloud Storage and use Vertex ML Metadata to track and organize the lineage of ML resources.
• Deploy trained models to production using online or batch prediction services on Vertex AI.
• Set up monitoring systems to detect model performance degradation, such as feature skew or data drift, and fine-tune alert thresholds.