AI Architect
4 days ago
Job Description Job Description Role *AI Architect Desired Experience Range 12 to 15 years Location of Requirement Pan India- Desired Skills -Technical/Behavioral Must-Have - AI/ML Expertise: Strong proficiency in Python, TensorFlow, PyTorch, Scikit-learn, OpenAI APIs, LangChain. - Cloud & DevOps: Experience with AWS SageMaker, Azure ML, Google Vertex AI, Docker, Kubernetes, CI/CD. - Big Data & Databases: Expertise in Hadoop, Spark, Kafka, SQL, NoSQL, Snowflake, Delta Lake. - MLOps & AI Deployment: Hands-on experience with MLflow, Kubeflow, Airflow, FastAPI, Flask, Streamlit. - AI Security & Compliance: Deep understanding of model interpretability, AI ethics, adversarial attacks, governance. Good-to-Have - Experience with Generative AI & LLMs (GPT, LLaMA, Stable Diffusion, DALLE, etc.). - Knowledge of Edge AI & AI-powered IoT solutions. - Experience with AutoML tools like Google AutoML, H2O.ai, DataRobot. - Familiarity with quantization, pruning, and optimization techniques for AI model efficiency. - Hands-on experience with vector databases (FAISS, Pinecone, Weaviate) and Retrieval-Augmented Generation (RAG) architectures. - Knowledge of Reinforcement Learning (RL), Bayesian Methods, and Time-Series Forecasting. - Experience with Graph Neural Networks (GNNs) and AI in cybersecurity. - Exposure to Blockchain & AI integration for secure and decentralized AI applications. - Familiarity with natural language processing (NLP) frameworks like Hugging Face Transformers Responsibility of / Expectations from the Role - AI Strategy & Architecture Development - Define and implement an enterprise AI architecture that aligns with business goals and IT strategies. - Develop AI roadmaps, best practices, and governance frameworks to ensure scalability, security, and efficiency. - Evaluate, recommend, and integrate cutting-edge AI/ML frameworks, tools, and platforms (AWS, Azure, GCP). - Establish best practices for MLOps, AI governance, and ethical AI practices. - AI Model Development & Deployment - Lead the design, development, and optimization of machine learning, deep learning, and generative AI solutions. - Oversee data preprocessing, feature engineering, and model optimization to ensure accuracy and efficiency. - Implement MLOps pipelines for model training, deployment, monitoring, and continuous improvement. - Work with software engineers to integrate AI solutions into production environments seamlessly. - Data Engineering & AI Infrastructure - Collaborate with data engineering teams to design robust data pipelines, warehouses, and lakes for AI consumption. - Optimize real-time and batch data processing architectures for AI model performance. - Ensure AI infrastructure is scalable, cost-effective, and cloud-native where applicable. AI Governance, Security & Compliance - Establish AI governance frameworks to ensure models are explainable, fair, and aligned with ethical standards. - Ensure compliance with global data privacy laws (GDPR, HIPAA) and AI risk management frameworks. - Monitor AI models for bias, drift, and performance degradation and implement proactive mitigation strategies. - Leadership & Collaboration - Act as a strategic advisor to executives and stakeholders on AI adoption and innovation. - Provide technical leadership and mentorship to AI engineers, data scientists, and cross-functional teams. - Conduct knowledge-sharing sessions, drive AI training initiatives, and foster a culture of AI excellence.
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