AI Solution Engineer

Job Description :

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