AIML Architect

AI/ML Architect A highly skilled and experienced AI/ML Architect to lead the design, development, and deployment of innovative AI/ML solutions. You will be responsible for translating business requirements into scalable, robust, and production-ready AI/ML architectures, primarily leveraging the Google Cloud Platform (GCP). You will be a technical leader, mentoring other engineers and driving best practices in ML Ops. Total Years of Experience: 8-15 Years Relevant Experience: 7+ Years in AI/ML Architecture and Development Key Responsibilities: Solution Architecture: Design and architect end-to-end AI/ML solutions tailored to specific business needs, ensuring scalability, performance, and security. Technical Leadership: Provide technical leadership and guidance to a team of AI/ML Engineers, Data Scientists, and Software Engineers. GCP Expertise: Leverage deep knowledge of Google Cloud Platform (GCP) services, including BigQuery, Cloud Composer, Vertex AI, and other AI/ML services, to build and deploy AI/ML models. Model Development & Deployment: Lead the development, deployment, and monitoring of machine learning models, ensuring their reliability and accuracy in production. ML Ops Implementation: Champion and implement ML Ops best practices, including CI/CD pipelines, automated model retraining, and robust monitoring systems. Technology Evaluation: Research and evaluate new AI/ML technologies and frameworks, recommending solutions that align with business goals and technical requirements. Collaboration: Collaborate with cross-functional teams, including product managers, data engineers, and business stakeholders, to define AI/ML strategies and deliver impactful solutions. Performance Optimization: Optimize AI/ML models and infrastructure for performance, cost, and scalability. Data Governance: Adhere to data governance and security policies, ensuring the responsible and ethical use of AI/ML technologies. Documentation: Create and maintain comprehensive documentation for AI/ML architectures, models, and deployment processes. Mentorship: Mentor junior engineers and data scientists, fostering a culture of learning and innovation. Required Skills and Experience: Machine Learning Expertise: Strong understanding of machine learning concepts, including supervised, unsupervised, and reinforcement learning. Programming Proficiency: Expertise in Python and experience with related libraries such as TensorFlow, PyTorch, scikit-learn, pandas, and NumPy. Deep Learning: Proven experience in developing and deploying deep learning models using frameworks like TensorFlow or PyTorch. Cloud Platform Experience: Extensive experience with Google Cloud Platform (GCP), including BigQuery, Cloud Composer, Vertex AI, and other AI/ML services. Data Engineering: Solid understanding of data engineering principles and experience with data pipelines and ETL processes. Statistical Analysis: Strong foundation in statistical analysis and data visualization techniques. Model Deployment & Monitoring: Hands-on experience with model deployment and monitoring in a production environment. ML Ops: Deep understanding and practical experience with ML Ops principles and tools, including CI/CD pipelines, model versioning, and automated retraining. Containerization & Orchestration: Experience with containerization technologies (e.g., Docker) and orchestration platforms (e.g., Kubernetes). Communication Skills: Excellent communication, presentation, and interpersonal skills. Problem-Solving Skills: Strong analytical and problem-solving skills with the ability to work independently and as part of a team. Experience in multiple Business Verticals: Experience in Banking/ Finance/ telecom/Retail/ technology etc is a plus. Text Analytics and Text Mining: Strong hold of concepts in Statistics and expertise in Machine Logs processing, text mining and text analytics. Passionate about AI/ML and its potential to transform businesses, results-oriented Continuous learner with a strong desire to stay up-to-date with the latest technologies. Excellent team player with a collaborative approach. Technical Skills: Programming Languages: Python, R (nice to have) ML Frameworks: TensorFlow, PyTorch, Keras, Sklearn Cloud Platforms: Google Cloud Platform (GCP) - Mandatory. AWS/Azure a plus. Big Data Technologies: Spark, PySpark Databases: SQL, NoSQL Tools: Docker, Kubernetes, CI/CD pipelines (Jenkins, GitLab CI, etc.) Concepts: Supervised, Un-supervised and Reinforcement Learning, Recommendation Systems, Computer Vision. Relevant Certifications (Preferred): Google Professional Cloud Architect Google AI/ML Certification