Job Title

Title: AI Solution EngineerLocation: India, RemoteFulltime.Job Description:What does a AI Solution Engineer do? As an AI Solution Engineer you will be responsible for developing, deploying, and implementing advanced AI-enabled applications for our highly sophisticated systems, ensuring compliance with security standards and delivering innovative and efficient solutions within a secured environment. Self-discipline and a strong desire to build applications with high integrity are essential for success in this role. What you will do: • Research and Innovation: Stay updated with the latest AI technologies, tools, and trends to continuously improve and innovate data conversion processes. • Documentation: Maintain comprehensive documentation of AI solutions, methodologies, and deployment processes. • Design and Develop AI Models: Implement AI solutions focused on automating and enhancing the core data conversion processes. • Data Handling: Work with large datasets, ensuring the integrity and security of sensitive information during the conversion process. • Secure Environment Compliance: Develop and deploy AI solutions in accordance with security protocols, ensuring all processes meet compliance standards. • Collaboration: Work closely with cross-functional teams including data scientists, software engineers, and business analysts to create integrated AI solutions. • Testing and Validation: Conduct rigorous testing and validation of AI models to ensure accuracy and reliability. • Performance Optimization: Continuously monitor and optimize AI models for efficiency and performance improvements. Perform application scoring and data aggregation What you will need to have: Programming Skills: Proficiency in programming languages such as Python, JS/NodeJS, and .NET Framework/Core C#. Machine Learning Frameworks: Familiarity with ML frameworks and libraries such as TensorFlow, PyTorch, Keras, or Scikit-Learn. Experience in selecting and implementing appropriate algorithms for specific tasks is highly valuable.Data Handling and Processing: Experience with data manipulation and analysis using tools like Pandas or NumPy. Understanding how to preprocess data, handle unstructured data, and create datasets for training models is crucial. Deep Learning: Knowledge of deep learning concepts and architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, especially for tasks related to image recognition, natural language processing, and more. Software Development Practices: Familiarity with software development methodologies, version control systems (like Git), and DevOps principles to ensure smooth integration and deployment of AI models. Cloud Computing: Experience with cloud-based services and platforms (e.g., AWS, Google Cloud, Azure) that provide tools for machine learning and AI deployment. System Design: Ability to design scalable AI systems, including understanding architecture patterns, APIs, and microservices for integrating AI models into broader applications. Problem-Solving: Strong analytical and problem-solving skills to identify the best AI solutions for various challenges and to troubleshoot issues that arise during implementation. Collaboration and Communication: Experience in working collaboratively with cross-functional teams, including data scientists, software engineers, and business stakeholders, to align AI solutions with business objectives. Hands-on Experience: 5-7+ years of technical implementation experience What would be great to have: • Experience in the Financial Services Industry and an understanding of relevant compliance standards and regulations. • Certification in AI/ML or relevant technologies. • Experience with reporting tools like Splunk, SSRS, Cognos, and Power BI