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 contemporary 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: - Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or a related discipline. - 5 to 10 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. - Familiarity with model tracking, experiment management, and registry tools such as MLflow, Vertex AI Model Registry, or equivalent. - Good understanding of feature stores, model monitoring, drift detection, logging, and production support for ML systems. - Experience with LLM ecosystem tools and frameworks such as LangChain, LangGraph, LlamaIndex, Hugging Face, or similar is preferred. - Knowledge of vector databases such as Pinecone, Weaviate, Chroma, Vertex AI Vector Search, or equivalent is a plus. - Strong understanding of cloud security, IAM policies, secrets management, governance, and responsible AI practices. - Excellent problem-solving, communication, and stakeholder collaboration skills in cross-functional delivery environments. - Exposure to scalable AI/ML deployments in enterprise settings, especially real-time and high-availability systems, will be an added advantage. .
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