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.
- Robust 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.

📌 AI/ML Engineer-5-9Yrs-PAN India (Maharashtra)
🏢 Crescendo Global Leadership Hiring India
📍 Maharashtra