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

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.