AI Solution Architect

Qualifications and Education Requirements: Must Have: Engineering graduate with 15+ years of experience in Enterprise Applications development. 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: o 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. o 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.

o 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.