Senior Machine Learning Engineer

Role - MLOps Engineer (GCP Specialization) Location - Remote(100%) Experience Required - 10+ years Mandatory skills Technical Skills: • Proficiency in programming languages such as Python. • Expertise in GCP services, including Vertex AI, Google Kubernetes Engine (GKE), Cloud Run, BigQuery, Cloud Storage, and Cloud Composer, Data proc or PySpark and managed Airflow. • Experience with infrastructure-as-code - Terraform. • Familiarity with containerization (Docker, GKE) and CI/CD pipelines, GitLab and Bitbucket. • Knowledge of ML frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps tools compatible with GCP (MLflow, Kubeflow) and Gen AI RAG applications. • Understanding of data engineering concepts, including ETL pipelines with BigQuery and Dataflow, Dataproc - Pyspark. Soft Skills: • Strong problem-solving and analytical skills. • Excellent communication and collaboration abilities. • Ability to work in a fast-paced, cross-functional environment Good to have skills: - • Experience with large-scale distributed ML systems on GCP, such as Vertex AI Pipelines or Kubeflow on GKE, Feature Store. • Exposure to Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) applications and deployment strategies. • Familiarity with GCP’s model monitoring tools and techniques for detecting data drift or model degradation. • Knowledge of microservices architecture and API development using Cloud Endpoints or Cloud Functions. • Google Cloud Professional certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect).