EE_Integration_ Solution Architect

JAVA

PYTHON

AWS CLOUD

AZURE

GOOGLE CLOUD

APACHE KAFKA

HADOOP

DATA MINING

MACHINE LEARNING

B.TECH/B.E

Role

AI Solution Architect / Solution Architect (12- 15 Years)

What awaits you/ Job Profile

• 
  Solution Design: Develop comprehensive architecture plans that incorporate AI technologies to solve complex business problems and enhance operational efficiency.
• AI Integration: Work with data scientists and engineers to integrate AI models and algorithms into business applications, ensuring scalability and performance.
• Stakeholder Collaboration: Engage with business leaders and technical teams to gather requirements and translate them into effective AI solutions.
• Technology Evaluation: Stay updated with the latest AI technologies and tools, evaluating their applicability to current and future projects.
• Quality Assurance: Implement best practices for AI model development and deployment, ensuring high-quality and reliable solutions.
• Risk Management: Identify potential risks associated with AI implementations and develop strategies to mitigate them.
• Performance Monitoring: Monitor the performance of AI solutions, using data-driven insights to optimize and improve outcomes

What should you bring along

• Proven experience in solution architecture and AI technologies.
• Strong understanding of machine learning, deep learning, and data analytics.
• Excellent leadership, communication, and organizational skills.
• Proficiency in AI frameworks and programming languages such as Python, TensorFlow, and PyTorch.

Must have skill

• 
  Machine Learning & Deep Learning: Proficiency in designing, training, and deploying machine learning models and deep learning architectures.
• Programming Languages: Strong coding skills in languages such as Python, R, Java, or C++.
• AI Frameworks: Experience with AI frameworks and libraries like TensorFlow, PyTorch, Keras, and Scikit-learn.
• Data Management: Expertise in data preprocessing, data mining, and data visualization techniques.
• Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud, especially their AI and machine learning services.
• Big Data Technologies: Knowledge of big data tools like Hadoop, Spark, and Kafka for handling large datasets.
• Natural Language Processing (NLP): Understanding of NLP techniques for processing and analyzing text data.

Good to have technical skills

• Strong analytical and problem-solving skills.
• Ability to manage multiple projects simultaneously.
• Excellent time management and organizational skills.
• Strong interpersonal skills and ability to work collaboratively across departments.