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.
