AI Engineer (GCP) is responsible for designing, building, deploying, and operating production‑grade AI/ML solutions on Google Cloud Platform (GCP). The role focuses on end‑to‑end AI lifecycle execution, including data preparation, model development, training, evaluation, deployment, and monitoring using current and hands‑on GCP services.

This role works closely with Data Engineers, Platform teams, and Business stakeholders to translate use cases into scalable, secure, and cost‑efficient AI solutions. Active, recent GCP experience is mandatory, with strong exposure to Vertex AI, BigQuery, and cloud‑native MLOps practices.

#	Key Accountabilities (WHAT)	Key Tasks (HOW)	Key Deliverables (RESULT)
1	AI/ML Solution Design & Architecture (GCP)	· Design end‑to‑end AI and ML solutions using GCP‑native services. · Select appropriate model architectures (ML/DL/LLMs) based on business needs. · Define data, training, inference, and deployment architectures. · Ensure solutions align with enterprise security, scalability, and reliability standards.	· GCP‑based AI solution architectures and design documents. · Approved technical designs for AI use cases. · Reusable AI/ML architectural patterns.
2	Model Development, Training & Evaluation (Vertex AI)	· Develop, train, fine‑tune, and evaluate ML/DL models using Vertex AI. · Implement feature engineering and data preprocessing pipelines. · Perform model validation, bias checks, and performance benchmarking. · Experiment with traditional ML and deep learning frameworks as required.	· Trained and validated AI/ML models. · Experiment tracking and model evaluation reports. · Reproducible training artifacts.
3	Data Integration & Feature Engineering (GCP)	· Work with BigQuery and cloud data pipelines to source training and inference data. · Design feature engineering strategies and feature stores. · Ensure data quality, consistency, and governance across AI workflows. · Collaborate closely with Data Engineers to optimize datasets for ML workloads.	· Clean, ML‑ready datasets and feature definitions. · Feature pipelines and reusable transformations. · Data validation and reconciliation outputs.
4	Continuous Improvement & AI Enablement	· Stay current with latest GCP AI/ML services and best practices. · Continuously optimize models, pipelines, and infrastructure. · Support teams with AI best practices, design reviews, and technical guidance. · Contribute to internal AI standards, accelerators, and reusable components.	· Improved model performance and operational efficiency. · AI best‑practice guidelines and reference implementations. · Knowledge sharing sessions and technical documentation.

A. Experience - (Minimum Knowledge to Perform the Job Effectively)

# Of Years (xx)

· 5+ years of experience in AI/ML engineering

· Minimum 3+ years of recent, hands‑on experience on Google Cloud Platform (mandatory)

Skills Required

Technical/Managerial

Technical:

· Current, hands‑on experience with GCP (mandatory, not theoretical or past exposure)

· Strong experience with Vertex AI (training, endpoints, pipelines, experiments).

· Solid knowledge of BigQuery for analytics and ML workloads.

· Proficiency in Python and ML/DL frameworks (e.g., TensorFlow, PyTorch, scikit‑learn).

· Strong understanding of data engineering concepts for ML use cases.

· Ability to translate business problems into AI/ML solutions.

Other Requirements (If Any)

· Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

· Relevant GCP certifications (e.g., Professional Machine Learning Engineer) are a strong plus.

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