Job Role: Google Cloud AI/ML Engineer
We are seeking an experienced and innovative Google Cloud AI/ML Engineer to design, build, and deploy AI and ML solutions on Google Cloud Platform (GCP). The ideal candidate will be responsible for developing cutting-edge AI/ML solutions to solve complex business problems, leveraging the suite of tools and technologies available in GCP.
Key Responsibilities
- Design, build, train, and deploy machine learning models using Vertex AI, while integrating Gemini for advanced multi-modal AI capabilities (e.g., text and image data processing).
- Leverage Google Clouds pre-built AI APIs (Vision AI, Natural Language AI, Translation AI) to accelerate development workflows.
- Work on end-to-end AI/ML pipelines, from data ingestion to model deployment, using Vertex Pipelines, Cloud Build, and other workflow automation tools.
- Automate scalable pipelines for end-to-end AI/ML workflows, including data preparation, model training, and monitoring, utilizing tools like Cloud Dataflow and BigQuery ML.
- Process, clean, and transform data to make it usable for modeling using Dataprep, BigQuery, and Cloud Data Fusion.
- Build efficient, secure, and scalable data pipelines with tools like Cloud Storage, BigQuery, and Cloud Dataflow to support ML workflows effectively.
- Experiment with Gemini's advanced generative AI and multi-modal capabilities in Generative AI Studio and Model Garden to unlock innovative AI applications.
- Use tools such as Vertex AI Workbench, Codey AI, and Gemini to simplify prototyping, streamline debugging, and accelerate AI/ML solution development.
- Empower development teams by integrating predictive and generative AI insights powered by Gemini into enterprise workflows.
- Develop generative AI features by leveraging Gemini, PaLM APIs, and Generative AI Studio for business-critical applications.
- Fine-tune machine learning models to improve accuracy, speed, and scalability, using Vertex AI AutoML and custom training environments.
- Monitor and retrain AI/ML models to ensure ongoing performance aligned with business needs, leveraging MLOps tools such as Vertex Pipelines, Vertex AI Model Monitoring, and Kubernetes Engine (GKE).
- Ensure scalability, security, and robustness of AI/ML models through Vertex AI Model Monitoring, ML Metadata Tracking, and other advanced tools.
- Seamlessly integrate AI/ML models into enterprise systems using Cloud Run, Cloud Functions, and custom APIs.
- Collaborate with interdisciplinary teams to ensure the successful development and implementation of AI/ML workflows aligned with organizational goals.
- Stay informed about the latest advancements in generative AI tools, Gemini, PaLM APIs, and GCP AI technologies, applying these innovations to improve solutions and services.
- Explore emerging AI/ML technologies on Google Cloud to expand organizational capabilities and achieve business objectives.
- Utilize Geminis advanced features to address challenges that require multi-modal capabilities, generative AI, or large language models (LLMs).
- Ensure compliance with Responsible AI practices, including fairness, transparency, and ethical considerations, using GCP tools like Explainable AI on Vertex AI and data governance features.
- Maintain compliance with data security, governance regulations, and organizational standards such as Google Cloud IAM, audit logging, and encryption in transit.
Required Skills/Qualifications
- 3+ years of experience in designing, developing, and deploying AI/ML models on Google Cloud Platform (GCP).
- Expertise in developing AI/ML solutions using key GCP tools, including Vertex AI, BigQuery ML, Gemini, and Cloud AI APIs like Vision, NLP, and Translation.
- Proficiency in leveraging Gemini for multi-modal AI applications and integrating generative AI solutions into practical workflows.
- Strong programming skills in Python, R, or Java, with experience in AI frameworks such as TensorFlow and PyTorch.
- Knowledge of data engineering tools, such as Cloud Dataflow, BigQuery, Cloud Pub/Sub, and Cloud Data Fusion, for building scalable pipelines.
- Hands-on experience with Vertex AI AutoML, Vertex Pipelines, and Vertex AI Model Monitoring for model lifecycle management and MLOps implementation.
- Proficiency in building and deploying ML models in serving environments such as GKE, Cloud Run, and Cloud Functions.
- Strong understanding of generative AI technologies, including Gemini, PaLM APIs, Codey AI, and Generative AI Studio, for enhanced productivity and AI-powered development.
- Familiarity with Responsible AI principles, leveraging GCP tools such as Explainable AI and following data fairness and bias management practices.
- Strong collaboration, communication, and problem-solving skills for working with technical teams and business stakeholders.
- Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or a related field.
- Relevant certifications like Google Professional Machine Learning Engineer, Google Cloud Professional Data Engineer.
Job Role: Google Cloud AI Solution Architect
Role Overview: We are looking for a Google Cloud AI Solution Architect with deep expertise in Google Cloud Platform (GCP) services to design and implement AI solutions on Google Cloud. This role requires proficiency in Google Cloud AI/ML tools, AI-powered development frameworks like Codey AI, and strategic AI integrations using Gemini, Generative AI Studio, Vertex AI, and PaLM APIs.
Key Responsibilities
- Design end-to-end AI/ML solutions using Google Cloud services, including Vertex AI, BigQuery ML, Cloud AI APIs (Vision AI, NLP AI, and Translation AI), and generative AI tools like Generative AI Studio, Gemini, and PaLM APIs, integrating capabilities like Codey AI for rapid prototyping and productivity.
- Architect scalable, secure, and high-performance AI systems that seamlessly integrate into cloud-native enterprise business applications.
- Assess business objectives and requirements to determine where AI/ML solutions and multi-modal AI capabilities like those provided by Gemini can deliver maximum value and ROI.
- Lead the development of AI/ML pipelines, including data preparation, model development, deployment, and monitoring, leveraging tools like Vertex Pipelines and Cloud Build.
- Integrate AI models into enterprise cloud-based architectures while ensuring security, robustness, and scalability.
- Implement scalable, efficient data pipelines using BigQuery, Cloud Dataflow, and Cloud Data Fusion for data transformation and preparation in AI/ML workflows.
- Leverage Geminis advanced multi-modal capabilities (e.g., processing text, images, and other data types) for innovative AI solutions, as well as Vertex AI Prediction for deploying and managing ML models.
- Provide strategic direction on enterprise AI adoption, aligning Google Cloud AI/ML solutions with organizational needs and using tools like Gemini for cutting-edge multi-modal implementations.
- Collaborate with data scientists, AI engineers, and other stakeholders to ensure architectural alignment and successful AI/ML solution delivery.
- Drive innovation by staying informed about Google's evolving AI/ML capabilities, including Generative AI advancements like Gemini and Responsible AI practices.
- Foster the adoption of LLM-based generative AI tools for enterprise AI development and processes, leveraging both PaLM APIs and Gemini.
- Advise on MLOps best practices using Vertex AI Pipelines, Cloud Build, and Google Kubernetes Engine (GKE) to automate and optimize model development, monitoring, and lifecycle management.
- Ensure alignment with compliance standards and data governance policies using GCP tools like Google Cloud IAM, Cloud Audit Logs, and Data Catalog for secure AI architecture.
Required Skills/Qualifications
- 5+ years of experience in designing and implementing AI/ML solutions, with a focus on Google Cloud technologies and enterprise-level deployments.
- Expertise in architecting AI/ML solutions using Google Cloud AI/ML services, including Vertex AI, BigQuery ML, Gemini, and Cloud AI APIs (Vision AI, NLP AI, Text-to-Speech).
- Proficiency in leveraging Generative AI tools, such as Gemini, PaLM APIs, Generative AI Studio, and Codey AI, for delivering cutting-edge generative and multi-modal AI solutions.
- Strong foundation in programming languages such as Python, R, and Java, with hands-on experience in TensorFlow and PyTorch for AI/ML development.
- Skilled in managing and optimizing AI workflows with tools like Vertex Feature Store, Vertex Experiments, and Vertex AI Model Monitoring.
- Hands-on experience with data tools such as BigQuery, Cloud Dataflow, Cloud Pub/Sub, and Cloud Storage for data preparation and analytics.
- Proficiency in deploying and managing scalable AI/ML models using frameworks like Google Kubernetes Engine (GKE) and Cloud Run.
- Experience with MLOps practices leveraging tools like Cloud Build, Vertex Pipelines, and Vertex Model Registry for CI/CD in AI/ML solutions.
- Strong knowledge of GCP DevOps practices, including containerization using Docker, orchestration using Kubernetes, and scalable architecture using GKE.
- Ability to provide strategic guidance on the adoption of generative AI tools like Gemini and PaLM APIs in enterprise-level projects to streamline AI-powered processes.
- Familiarity with Responsible AI principles, including explainability, bias mitigation, and fairness, leveraging tools like Explainable AI for Vertex AI.
- Strong problem-solving abilities and a deep understanding of integrating AI into real-world business environments.
- Excellent collaboration and communication skills to engage with technical and business stakeholders effectively.
- Bachelors or Masters degree in Computer Science, AI/ML, Data Science, or a related field.
- Relevant certifications like Google Professional Machine Learning Engineer, Google Cloud Professional Data Engineer, or TensorFlow Developer Certification.
Google Cloud Data Architect
Job Summary
Key Responsibilities
Required Skills/Qualifications
Google Cloud Data Engineer
Job Summary
Key Responsibilities
Required Skills/Qualifications
Compensation:
Day rate of INR10,000 to INR15,000 per day
Permanent salary between 20 lakh to 50 lakh, depending on the experience
Working from Home
Opportunity to travel onsite