Infor

AI and ML are transforming the software industry and all other business domains. The release of Language models like ChatGPT have accelerated the transition to AI/ML for all applications. The business goal is the transformation of the technology platform components to leverage AI and ML to provide increased productivity and value for customers.

Position Summary:

·       This is an Individual Contributor role where your responsibility will be to contribute to development of research projects in AI & ML.

·       The fields of research will include (but not limited to) implementation of foundation models, large language models, multimodal embeddings, conversational systems, image processing, optical character recognition, human-machine teaming, sentiment analysis, and/or generative AI.

·       You will team with other development partners and drive foundational AI research in different products like Digital Assistant and Document Capture.

·       You will work with all component partners to implement the vision and roadmap leveraging AI and ML in different products.

Essential Duties: 

As an AI and ML engineer specializing in technologies like image processing, personal assistant, risk assessment, etc., your essential duties would involve a combination of research, development, and implementation tasks.

Here are some key responsibilities you can expect in this role:

·       Research: Stay up to date with the latest advancements and research trends in the relevant fields. Conduct literature reviews, analyze existing algorithms, and explore cutting-edge techniques in advanced DL areas. 

·       Algorithm Development: Design and develop novel AI and ML algorithms specifically tailored for image processing tasks, such as object detection, image recognition, image segmentation, and image generation. Create efficient algorithms for personal assistant functions, including speech recognition, natural language understanding, dialogue management, and personalized recommendations.

·       Data Collection and Preparation: Identify and gather relevant datasets for training and testing image processing and personal assistant models. Clean, preprocess, and augment the data to ensure its quality and suitability for the intended tasks.

·       Model Training and Evaluation: Utilize machine learning frameworks and tools to train and fine-tune models for image processing and personal assistant tasks. Experiment with different architectures, loss functions, and optimization techniques. Evaluate the performance of models using appropriate evaluation metrics and benchmarks including performance optimizations.

·       Performance Optimization: Optimize models and algorithms for efficiency, speed, and resource usage. Implement techniques like model compression, quantization, and parallel processing to enable real-time image processing and fast responses from the personal assistant system.

·       Integration and Deployment: Collaborate with software engineers and developers to integrate AI and ML models into larger systems or applications. Ensure seamless integration and robustness of the image processing and personal assistant functionalities. Deploy models in production environments and handle any issues that arise.

·       Continuous Improvement: Continuously evaluate and enhance the performance and capabilities of the deployed AI products. Keep track of user feedback and re-train & iterate on the models and algorithms to address limitations and improve user experience.

·       Collaboration and Communication: Collaborate with cross-functional teams, including researchers, engineers, designers, and product managers, to define requirements, align objectives, and deliver high-quality solutions. Effectively communicate research findings, technical concepts, and project progress.

Basic Qualifications & Skills: 

To be an AI and ML expert, you would require a combination of educational qualifications, technical skills, and personal attributes. Here are some qualifications that would be beneficial for such a role: 

Educational Background:

·       Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related field.

• Specialization or coursework in AI, ML, Statistics & Probability, DL, Computer Vision, Signal Processing, or NLP/NLU is a plus.

Strong Foundation in AI and ML:

·       Experience in implementing AI/ML models in Big Data platforms for business problems.

• In-depth understanding of ML algorithms, including supervised and unsupervised learning, reinforcement learning, deep learning, and probabilistic models.

Programming and Tools:

·       Proficiency in programming languages commonly used in AI and ML, such as Python or R & querying languages like SQL.

• Experience in Cloud computing infrastructures like AWS Sagemaker or Azure ML for implementing ML solutions is highly preferred.
• Experience with relevant libraries and frameworks, such as scikit-learn, Keras, TensorFlow, PyTorch, or NLTK is a plus.
• Ability to work with large datasets, data preprocessing, and data wrangling.

Preferred Skills:

Image Processing Expertise:

• Proficiency in image processing techniques, including image filtering, feature extraction, image segmentation, object detection, and image recognition.
• Knowledge and understanding of CNN, Transformers and other related algorithms.
• Familiarity with computer vision libraries and frameworks such as OpenCV, TensorFlow, PyTorch, Keras, Scikit-learn, and Scikit-image.

Personal Assistant Systems:

• Familiarity with natural language processing (NLP) techniques, including text classification, named entity recognition, sentiment analysis, and language modeling.
• Knowledge and understanding of Transformers and fine-tuning of LLMs is preferred.
• Knowledge of speech recognition, text-to-speech synthesis, dialogue systems, and conversational agents.

Problem-Solving and Analytical Skills:

• Strong analytical and critical thinking abilities to identify and define problems, formulate hypotheses, and design experiments.
• Capacity to break down complex problems into manageable tasks and propose effective solutions.
• Attention to detail and ability to analyze and interpret data accurately.

Communication and Collaboration:

• Excellent written and verbal communication skills to articulate complex technical concepts to both technical and non-technical stakeholders.
• Collaborative mindset and ability to work effectively in interdisciplinary teams.
• Strong presentation skills to deliver research findings and project updates.

Continuous Learning:

• Eagerness to stay updated with the latest advancements in AI and ML through self-study, research papers, conferences, or workshops.
• Willingness to adapt to new technologies, tools, and methodologies.