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.
