Machine Learning Engineer

Key Responsibilities:

• GCP AI Ecosystem Expertise: Serve as a subject matter expert for Google Cloud Platform's AI services, with a particular focus on Gemini Enterprise, AI Studio, and NotebookLM.

• Solution Architecture & Optimization: Design, implement, and optimize scalable and cost-effective ML solutions leveraging GCP managed services such as Vertex AI, Cloud Run, Cloud Functions, and various database services (e.g., Cloud SQL, Firestore, BigQuery).

• Gemini Enterprise & Agent Development: Provide advanced technical support and guidance for Gemini Enterprise implementations, including prompt engineering, model fine-tuning, and the development and deployment of AI agents using relevant Agent Development Kits.

• Troubleshooting & Problem Solving: Diagnose and resolve complex technical issues related to GCP AI services, ML model deployments, data pipelines, and infrastructure.

• Customer Query Handling: Act as a primary point of contact for internal teams and clients, addressing technical queries, providing prompt guidance, and offering expert advice on leveraging GCP AI capabilities effectively.

• Operational Support: Manage and respond to incoming support requests via JIRA and designated MS Teams channels ("Gemini AI Support"), ensuring timely resolution and adherence to service level agreements (SLAs).

• Best Practices & Governance: Advocate for and implement best practices in MLOps, security, cost optimization, and compliance within the GCP environment.

• Documentation & Knowledge Sharing: Create and maintain comprehensive technical documentation, runbooks, and knowledge base articles to empower internal teams and clients.

• Collaboration & Mentorship: Collaborate with data scientists, software engineers, and product managers to integrate AI solutions, and mentor junior engineers on GCP AI best practices.

• Stay Current: Continuously research and evaluate new GCP AI services, features, and industry trends to recommend innovative solutions.

Required Skills & Experience:

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related quantitative field.

• 5+ years of professional experience as a Machine Learning Engineer, MLOps Engineer, or a similar role with a strong focus on cloud platforms.

• Deep expertise in Google Cloud Platform (GCP) services, including:

• Vertex AI (especially for model training, deployment, and monitoring)

• Gemini Enterprise (hands-on experience with its capabilities and integration)

• Cloud Run and Cloud Functions for serverless ML inference and event-driven architectures.

• GCP Database services (e.g., Cloud SQL, Firestore, BigQuery) for data storage and retrieval.

• Networking and Security concepts within GCP.

• Proven experience with large language models (LLMs) and generative AI, specifically with Google's Gemini family of models.

• Hands-on experience with Agent Development Kits (ADKs) or frameworks for building conversational AI agents.

• Strong programming skills in Python, including relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).

• Experience with MLOps practices and tools for CI/CD, version control (Git), and automated testing of ML pipelines.

• Excellent problem-solving, analytical, and debugging skills.

• Strong communication and interpersonal skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.

• Ability to work independently and as part of a team in a fast-paced, dynamic environment.