AI/ML Engineer

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.