AI Architect

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, DALL·E, 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.