Data Scientist 

Job Description

Key Responsibilities & Skillsets:

– Design, build, and maintain scalable MLOps and LLMOps pipelines for deploying, monitoring, and managing machine learning and generative AI solutions in GCP.

– Develop and operationalize end-to-end ML lifecycle workflows including data ingestion, feature engineering, model training, validation, deployment, and monitoring.

– Build and manage LLMOps workflows for Large Language Models, including prompt management, RAG pipelines, vector databases, model evaluation, guardrails, and observability.

– Deploy and manage ML and GenAI workloads using Vertex AI, GKE, Cloud Run, and other GCP-native services.

– Implement CI/CD and CT pipelines for ML models and LLM-based applications using tools such as GitHub Actions, Cloud Build, Jenkins, or Terraform.

– Collaborate with Data Scientists, ML Engineers, Data Engineers, and Product teams to productionize machine learning and GenAI use cases.

– Establish model monitoring frameworks for drift detection, latency tracking, usage analytics, output quality, and operational performance.

– Build reusable and scalable infrastructure for experimentation, model versioning, artifact tracking, and automated retraining.

– Manage model registry, feature store integration, metadata tracking, and pipeline orchestration using modern MLOps tooling.

– Implement secure and responsible AI practices including access control, governance, model auditability, and compliance with enterprise policies.

– Optimize inference workloads for performance, cost, scalability, and reliability across batch and real-time serving environments.

– Research and adopt best practices in MLOps, LLMOps, GenAI deployment, and GCP architecture to continuously improve platform capabilities.

– Support debugging, troubleshooting, and incident resolution across ML platforms, deployment pipelines, and production workloads.

– Document architecture, pipeline design, deployment processes, and operational standards for internal teams and stakeholders.

Candidate Profile:

– Bachelors or Masters degree in Computer Science, Data Engineering, Artificial Intelligence, or a related discipline.

– 5 to 7 years of experience in Machine Learning Engineering, MLOps, ML Platform Engineering & LLMOps.

– Strong hands-on experience in MLOps on GCP, especially with services such as Vertex AI, BigQuery, GCS, Cloud Functions, Cloud Run, GKE, Pub/Sub, and IAM.

– Solid experience in building and managing LLMOps workflows, including RAG pipelines, vector databases, prompt orchestration, evaluation frameworks, and LLM observability.

– Proficiency in Python, SQL, and scripting for automation and pipeline orchestration.

– Strong knowledge of containerization and orchestration tools such as Docker and Kubernetes.

– Experience with ML workflow orchestration and pipeline tools such as Kubeflow, Vertex AI Pipelines, Airflow, or similar frameworks.

– Hands-on experience with CI/CD, Infrastructure as Code, and DevOps tools such as Terraform, GitHub Actions, Cloud Build, Jenkins, and Git.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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