AI Design Engineer

Key Responsibilities :

- Design, develop, train, and optimize machine learning and deep learning models for real-world business applications.

- Build, fine-tune, and deploy Generative AI solutions using Foundation Models and Large Language Models (LLMs).

- Develop AI-powered applications involving one or more domains, including :

1. Natural Language Processing (NLP)

2. Computer Vision

3. Speech Processing

4. Audio Intelligence

5. Video Analytics

- Understand and implement LLM building blocks such as tokenization, embeddings, attention mechanisms, transformers, prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning techniques.

- Build scalable AI pipelines for data preprocessing, model training, evaluation, inference, and deployment.

- Work with structured and unstructured datasets to develop predictive and generative AI solutions.

- Optimize model performance for accuracy, latency, scalability, and cost efficiency.

- Collaborate with data scientists, software engineers, product managers, and business stakeholders to translate business requirements into AI-driven solutions.

- Stay updated with the latest advancements in Machine Learning, Deep Learning, Generative AI, and Foundation Models.

- Document models, experiments, and best practices to ensure maintainability and knowledge sharing.

Required Skills & Qualifications :

Technical Skills :

- Strong foundation in Machine Learning, Artificial Intelligence, and Deep Learning.

- Strong understanding of Generative AI, Foundation Models, Large Language Models (LLMs), Transformer architecture, and tokenization techniques.

- Hands-on experience with PyTorch and Machine Learning libraries and frameworks.

- Experience developing AI applications in one or more of the following domains :

1. Text/NLP

2. Computer Vision

3. Speech Recognition

4. Audio Processing

5. Video Analytics

- Knowledge of model training, fine-tuning, evaluation, and deployment.

- Familiarity with model optimization and inference techniques.

- Understanding of prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) is highly desirable.

- Experience with version control systems such as Git.

- Familiarity with cloud platforms (AWS, Azure, or GCP) and MLOps tools is an added advantage.

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field.

- Experience working with open-source or commercial LLMs such as Llama, Mistral, GPT, or similar Foundation Models.

- Exposure to model deployment using APIs, Docker, Kubernetes, or cloud-native AI services.

- Understanding of AI ethics, responsible AI, and model governance.

Desired Candidate Profile :

- Strong analytical and problem-solving abilities.

- Passion for AI innovation and continuous learning.

- Excellent communication and collaboration skills.

- Ability to work independently as well as in cross-functional teams.

- Strong debugging and optimization skills for machine learning models.

- Ability to adapt quickly to emerging AI technologies and frameworks.

(ref:hirist.tech)