Principal Architect - AI

Design and Architect:

• Strong in Machine Learning Algorithms

• Strong in Machine Learning Algorithms

• Data Engineering and ETL/ELT

• Data cleaning, preprocessing and EDA

• Feature Engineering

• Data Splitting and encoding

• MLOps (Model versioning, Training, experimenting, deployment and monitoring)

• Python, Pandas, TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, Matplotlib, R, Scala, Java, etc.

• Git, DVC, MLFlow, Kubernetes, Kubeflow, Docker, Containers, CI/CD deployments, Apache Airflow

• Databricks, Snowflake, Salesforce, SAP, AWS/Azure/GCP Data Cloud Platforms

• AWS SageMaker, Google AI Platform, Azure Machine Learning

AWS Cloud Foundry, AWS Bedrock, Azure ML Services

• Model Design and Optimization

• LLMs models (OpenAI, BERT, LLaMA, Gemini etc.)

• RDBMS, No SQL database, Vector DB

• RAG Pipelines

• AI Agent Frameworks

• AI agent authentication and Deployment

• AI security and compliance

• Prompt Engineering

• The role requires extensive expertise in Artificial Intelligence (AI), Data Engineering, and Lambda architectures, each integral to the overall success in data-driven environments.
• Candidates should possess advanced knowledge of AI, with the ability to implement machine learning algorithms and develop predictive models that can transform data into actionable insights.
• Proficiency in Data Engineering is essential, demonstrating the capability to design, construct, and manage scalable data pipelines that ensure the availability and robustness of data for analysis and modeling.
• Familiarity with Lambda architectures is also critical, as it allows for the processing of data in real-time and batch modes, facilitating the development of responsive applications.
• Candidates should have a minimum of 5 years to 30 years of experience, showcasing their depth of understanding and application of these skills in diverse settings.
• A practical understanding of integrating these technologies in cloud environments, particularly using AWS and Azure resources, is highly regarded, as it forms the backbone of modern AI solutions.
• The educational background required for this role includes a Master of Technology (M.Tech) in Artificial Intelligence or Machine Learning, or a Post Graduate Diploma in Data Science or Data Analytics.
• Additionally, possessing certifications such as the AWS Certified Machine Learning – Specialty and Microsoft Certified: Azure Data Scientist Associate is preferred, as they signify a professional level of expertise in relevant technologies.
• Design and architect AI solutions by leveraging advanced machine learning algorithms and data engineering principles.
• Lead the development of robust ETL/ELT processes and oversee data cleaning, preprocessing, and exploratory data analysis (EDA) to ensure high-quality datasets.
• Implement feature engineering techniques and manage data splitting and encoding for optimal model performance.
• Drive MLOps practices including model versioning, training, experimenting, deployment, and monitoring to ensure seamless integration of AI models into production.
• Collaborate with cross-functional teams utilizing tools like Python, Pandas, TensorFlow, PyTorch, and Scikit-learn to develop scalable AI systems.
• Oversee deployment processes using technologies such as Docker, Kubernetes, and CI/CD pipelines, while managing data workflows on platforms like Databricks and Snowflake.
• Design and optimize models for various AI applications, including LLMs and agent frameworks, while ensuring compliance with AI security standards.
• Mentor junior team members, fostering a culture of continuous learning and innovation.

• This role necessitates a strong foundation in AI, particularly in implementing advanced machine learning algorithms and data processing techniques.
• Candidates should possess advanced proficiency in AI, capable of designing and developing AI-driven solutions that leverage large datasets, and demonstrate a thorough understanding of supervised and unsupervised learning methodologies.
• Java experience is essential for building scalable applications and integrating AI components into existing systems.
• A deep understanding of Java frameworks and libraries will be necessary to construct efficient data pipelines and manage backend services.
• Candidates are expected to apply their expertise in Data Engineering to build robust data architectures that support AI applications, ensuring data quality and accessibility.
• Experience with AWS Lambda is critical for creating serverless architectures, enabling scalable and cost-effective machine learning solutions.
• Candidates should be adept at utilizing Lambda for processing data streams and executing functions triggered by events in real-time.
• To excel in this role, candidates should have a Master of Technology (M.Tech) in Artificial Intelligence or Machine Learning, which provides the necessary theoretical knowledge and practical skills for tackling complex data-driven challenges.
• Preferred qualifications include the AWS Certified Machine Learning – Specialty certification, which demonstrates a strong understanding of AWS cloud services and best practices in machine learning deployment.

• Design and architect advanced AI solutions by leveraging machine learning algorithms and data engineering principles.
• Lead the development of robust ETL/ELT processes, ensuring high-quality datasets through data cleaning, preprocessing, and exploratory data analysis (EDA).
• Implement feature engineering techniques and manage data splitting and encoding to enhance model performance.
• Drive MLOps practices, including model versioning, training, experimenting, deployment, and monitoring, ensuring seamless integration of AI models into production environments.
• Collaborate with cross-functional teams using tools like Python, Pandas, TensorFlow, PyTorch, and Scikit-learn to develop scalable AI systems.
• Oversee deployment processes utilizing Docker, Kubernetes, and CI/CD pipelines, while managing data workflows on platforms such as Databricks and Snowflake.
• Design and optimize models for various AI applications, including LLMs and agent frameworks, while ensuring compliance with AI security standards.
• Mentor junior team members, fostering a culture of continuous learning and innovation, and contribute to the overall strategy and vision of AI initiatives within the organization.