Freelance Machine Learning Engineer (ML Engineer - GenAI / LLM / Vertex AI)

Job Description We are hiring an experienced Machine Learning Engineer (MLE) to design, develop, and deploy production-grade AI/ML applications that drive business innovation and insights. The ideal candidate is a hands-on builder with strong Python engineering skills, real-world experience delivering LLM, RAG, and GenAI solutions, and expertise in deploying scalable ML systems on Google Cloud Platform (GCP). Required Skills '05; Python (Must Have) '05; Google Cloud Platform (GCP) '05; Vertex AI (Must Have) '05; Machine Learning Model Development & Deployment '05; Data Preprocessing & Feature Engineering '05; Model Evaluation & Performance Optimization '05; BigQuery '05; NoSQL Databases (MongoDB preferred) '05; Git Version Control 🤖 GenAI / LLM Experience '05; Production experience with LLM applications '05; Retrieval-Augmented Generation (RAG) '05; Prompt Engineering '05; Agentic AI Workflows '05; LLM Evaluation Frameworks '05; Vector Databases and Semantic Search '05; AI Application Monitoring & Reliability &01; Preferred Skills

• Azure OpenAI
• GKE (Google Kubernetes Engine)
• Docker
• Dataflow
• Apache Kafka / PubSub
• Apache Spark, Ray, or Dask
• MLOps & CI/CD Pipelines 🗄 Databases & Data Platforms
• BigQuery
• Spanner
• MongoDB
• Relational Databases Key Responsibilities
• Design and develop scalable AI/ML solutions.
• Build and deploy production-grade LLM and RAG applications.
• Develop backend APIs and ML services using Python.
• Deploy and manage ML workloads on GCP and Vertex AI.
• Work closely with Data Scientists, Product Managers, and Engineering teams.
• Implement model evaluation, monitoring, and optimization strategies.
• Build scalable data pipelines and ML workflows.
• Drive architecture decisions and technical best practices. Required Experience
• Experience in Machine Learning Engineering experience.
• Strong Python software engineering background.
• Hands-on GCP and Vertex AI experience.
• Experience with BigQuery and NoSQL databases.
• Experience deploying ML applications to production environments.
• Strong understanding of ML lifecycle and MLOps principles. Preferred Experience
• Production LLM/RAG deployments.
• Agentic AI applications.
• Kubernetes (GKE).
• Docker containers.
• Streaming systems (Kafka, Pub/Sub).
• Google Professional Data Engineer certification.