AI Solution Architect
2 days ago
Architect AI/ML Solutions: Design and implement end-to-end AI/ML architectures on cloud platforms (AWS, Azure, or GCP), ensuring optimal performance, scalability, and security.
Leverage Generative AI: Implement cutting-edge generative AI models using cloud-native services like AWS Bedrock , Azure AI Studio , and GCP Vertex AI Generative AI Studio for use cases such as natural language generation, image generation, and creative applications.
Model Deployment & Scaling: Deploy AI models at scale using cloud-based tools and frameworks, with a focus on performance optimization and cost efficiency.
MLOps Implementation: Implement MLOps practices to automate the end-to-end lifecycle of machine learning models, including model versioning, deployment pipelines, monitoring, and automated retraining. Utilize cloud-native tools for CI/CD, such as AWS CodePipeline , Azure DevOps , and Google Cloud Build .
Model Registry Management: Manage model registry and version control using tools like AWS SageMaker Model Registry , Azure Machine Learning Model Registry , or GCP Vertex AI Model Registry to track model versions and ensure governance.
AI & ML Infrastructure Design: Design infrastructure using cloud services for large-scale AI and machine learning workloads, including managed services, containerization (e.g., Kubernetes , Docker ), and serverless computing (e.g., AWS Lambda , Azure Functions , Google Cloud Functions ).
Big Data and Data Engineering: Work with cloud data lakes, ETL pipelines, and distributed data systems to support data needs for AI/ML models. Use services like AWS Glue , Azure Data Factory , GCP Dataflow , and BigQuery .
Performance Tuning & Cost Optimization: Optimize AI workloads for cost and performance using cloud services, including Elastic Inference (AWS), Azure Virtual Machines for AI (Azure), and Google Cloud AI Infrastructure .
Security & Compliance: Ensure that AI/ML solutions are secure, compliant with industry standards, and follow best practices in cloud security, including data encryption, identity and access management (IAM), and auditing.
Collaborate with Cross-Functional Teams: Work closely with data scientists, business stakeholders, and engineering teams to build AI solutions that address business challenges and leverage cloud-native AI capabilities.
Qualifications and Education Requirements:
Must Have:
Engineering graduate with 15+ years of experience in Enterprise Applications development.
Bachelor’s or Masters degree in Information Technology, Computer Science, or a related field.
Experience in designing AI applications and knowledge of machine learning frameworks like TensorFlow or PyTorch.
Design, develop and oversee the implementation of end-to-end AI solutions.
Self-starter with an in-depth hands-on work experience with large-scale implementations on AWS or Azure. Knowledgeable on Google Cloud Platform.
Hands on expertise in design applications using Enterprise Architectural Patterns.
Expertise in full-stack development using technologies such as Python, Java, C#, Angular, React.
Should have working exposure in tools like Rally, Jira, Collobrator, Coverity, Valgrind tools.
Experience helping development teams build and deploy micro services-based applications in public cloud Experience with Continuous Integration & Continuous Deployment tools and processes.
Should be very good in Verbal & Written Communication.
Cloud Platform Expertise : Hands-on experience with one or more of the following cloud platforms and their AI/ML tools:
AWS : Expertise with services like Amazon SageMaker , AWS Bedrock , AWS Lambda , Amazon S3 , Amazon EC2 , SageMaker Model Registry , and AWS Glue for data engineering and model deployment.
Azure : Expertise with services like Azure AI Studio , Azure Machine Learning , Azure AI Search, GPT, Azure Kubernetes Service (AKS) , Azure Cognitive Services , Azure Functions , and Azure Machine Learning Model Registry .
Google Cloud : Expertise with services like GCP Vertex AI , GCP Vertex AI Generative AI Studio , Gemini, TensorFlow Extended (TFX) , Google Kubernetes Engine (GKE) , BigQuery , Cloud Functions , and Vertex AI Model Registry .
Generative AI Expertise : Experience with Generative AI models, including text generation (e.g., GPT), image generation (e.g., DALL-E), or other creative models, leveraging cloud-native tools such as AWS Bedrock , Azure AI Studio , GCP Vertex AI Generative AI Studio, GPT, Gemini, RAG techniques like Langchain, Vector Database like Chroma DB, Pinecone etc .
MLOps Implementation : Proficient in MLOps workflows, including model training, deployment, monitoring, versioning, and automation. Experience with tools such as MLflow , Kubeflow , Terraform , Jenkins , and Docker .
Model Registry & Versioning : Experience with model registry services like SageMaker Model Registry , Azure Machine Learning Model Registry , and Vertex AI Model Registry to track and govern models throughout their lifecycle.
Data Engineering & Big Data : Proficiency in data engineering best practices, data pipeline design, and management of big data infrastructure in cloud environments, including tools like Apache Kafka , Apache Spark , AWS Glue , Azure Data Factory , and Google Cloud Dataflow .
DevOps & Cloud Infrastructure : Familiarity with DevOps principles and infrastructure-as-code (IaC) tools like Terraform , CloudFormation , and Kubernetes for managing cloud infrastructure.
Security & Compliance : Deep understanding of cloud security principles, including IAM , data encryption , and governance best practices.
Programming & AI Frameworks : Strong programming skills in languages such as Python , Java , or R , and experience with AI/ML libraries and frameworks (e.g., TensorFlow , PyTorch , Keras , Scikit-learn ).
Preferred Skills
Experience in Google Cloud Platform
Experience of working in Media and Communication / broadcast business domain
Additional Notes
Certification in AI and Architecture area is plus.
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