AI ML & Data Architect

Job Summary We are seeking an experienced

AI/ML & Data Architect with

6–8 years of hands-on experience in designing enterprise-grade AI/ML solutions and modern data platforms. The ideal candidate will have strong expertise in AI/ML architecture, Generative AI, cloud-native data engineering, and scalable data ecosystems. This role involves defining end-to-end AI and data architecture, leading technical solution design, and driving the implementation of intelligent, data-driven applications across the organization.

Key Responsibilities

• Design and architect scalable AI/ML and data solutions aligned with business objectives.
• Define enterprise AI architecture using Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs).
• Design modern data platforms including Data Lakes, Data Warehouses, and Lakehouse architectures.
• Architect scalable ETL/ELT pipelines for batch and real-time data processing.
• Lead the implementation of Retrieval-Augmented Generation (RAG), AI Agents, and enterprise AI solutions.
• Define MLOps and DataOps frameworks for model lifecycle management and automated deployments.
• Design cloud-native solutions on Google Cloud Platform (Preferred), AWS, or Azure.
• Ensure data governance, security, scalability, reliability, and performance across AI and data platforms.
• Collaborate with cross-functional teams, stakeholders, and business leaders to translate business requirements into technical architectures.
• Mentor engineering teams and establish best practices for AI, data engineering, and cloud architecture.
• Evaluate emerging AI technologies and recommend innovative solutions for enterprise adoption.

Technical Skills

• Programming: Python, SQL, Java/Scala
• AI/ML: Machine Learning, Deep Learning, Generative AI, LLMs, NLP, Computer Vision, Prompt Engineering, RAG, AI Agents
• Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain, LlamaIndex
• Data Engineering: ETL/ELT, Apache Spark, PySpark, Kafka, Airflow, Data Lakes, Data Warehousing
• Databases: PostgreSQL, MySQL, MongoDB, BigQuery, Snowflake
• Cloud: Google Cloud Platform (Vertex AI, BigQuery, Dataflow, Cloud Storage), AWS/Azure
• MLOps & DevOps: Docker, Kubernetes, MLflow, Git, CI/CD, Jenkins
• APIs & BI: REST APIs, FastAPI, Power BI, Tableau
• Architecture: Cloud Architecture, Solution Design, Data Modeling, Data Governance, Microservices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
• 6–8 years of experience in AI/ML, Data Engineering, or Solution Architecture.
• Proven experience designing enterprise AI and cloud-based data platforms.
• Strong expertise in Google Cloud Platform (preferred), AWS, or Azure.
• Experience deploying AI/ML models into production using MLOps practices.
• Strong knowledge of distributed data processing and modern data architectures.
• Excellent problem-solving, leadership, and stakeholder management skills.

Preferred Certifications

• Google Professional Machine Learning Engineer
• Google Professional Data Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Azure AI Engineer Associate
• Databricks Certified Data Engineer Professional