Senior AI-ML Developer/Architect
Job Summary
Job Description
We are seeking a highly skilled and motivated AI-ML Lead with expertise in Generative AI and LLMs. You will play a crucial role in developing and implementing cutting-edge generative models and algorithms to solve complex problems and generate high-quality outputs. Responsibilities include researching advancements in generative AI, designing and optimizing generative models, processing large-scale datasets, training and evaluating models, collaborating with cross-functional teams, solving challenges in generative AI, and documenting research findings. Requires a Master's or Ph.D. in Computer Science or AI, proven experience with deep learning frameworks, proficiency in Python, strong analytical and communication skills, and a track record of research contributions in generative AI.
Must have:
- Master's or Ph.D. in Computer Science/AI
- Deep learning and generative models expertise
- Experience with LLMs, GPTs, GANs, VAEs
- Proficiency in Python
- Strong math and stats skills
- Excellent communication and teamwork
Good to have:
- Computer vision background
- Experience with TensorFlow/PyTorch
- Large-scale dataset handling
- Cloud computing/GPU acceleration experience
- Publications in top AI conferences/journals
- Active participation in AI research community
13 skills required
Job Details
About the job
We are seeking a highly skilled and motivated AI-ML Lead with expertise in Generative AI and LLMs to join our team. As a Generative AI and LLM Expert, you will play a crucial role in developing and implementing cutting-edge generative models and algorithms to solve complex problems and generate high-quality outputs. You will collaborate with a multidisciplinary team of researchers, engineers, and data scientists to explore innovative applications of generative AI across various domains.
Responsibilities:
- Research and Development: Stay up-to-date with the latest advancements in generative AI, including LLMs, GPTs, GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and other related techniques. Conduct research to identify and develop novel generative models and algorithms.
- Model Development: Design, develop, and optimize generative models to generate realistic and diverse outputs. Implement and fine-tune state-of-the-art generative AI architectures to achieve desired performance metrics.
- Data Processing and Preparation: Collect, preprocess, and curate large-scale datasets suitable for training generative models. Apply data augmentation techniques and explore strategies to handle complex data types and distributions.
- Training and Evaluation: Train generative models using appropriate deep learning frameworks and libraries. Evaluate model performance using quantitative and qualitative metrics. Iterate and improve models based on feedback and analysis of results.
- Collaboration: Collaborate with cross-functional teams, including researchers,
- engineers, and data scientists, to understand project requirements, define objectives, and identify opportunities to leverage generative AI techniques. Provide technical guidance and support to team members.
- Innovation and Problem Solving: Identify and tackle challenges related to generative AI, such as mode collapse, training instability, and generating diverse and high-quality outputs. Propose innovative solutions and approaches to address these challenges.
- Documentation and Communication: Document research findings, methodologies, and model architectures. Prepare technical reports, papers, and presentations to communicate results and insights to both technical and non-technical stakeholders.
Requirements:
- Education: A Master's or Ph.D. degree in Computer Science, Artificial Intelligence, or a related field. A strong background in deep learning, generative models, and computer vision is preferred.
- Experience: Proven experience in designing and implementing generative models using deep learning frameworks (e.g., TensorFlow, PyTorch). Demonstrated expertise in working with GPTs, GANs, VAEs, or other generative AI techniques. Experience with large-scale dataset handling and training deep neural networks is highly desirable.
- Technical Skills: Proficiency in programming languages such as Python, and familiarity with relevant libraries and tools. Strong mathematical and statistical skills, including linear algebra and probability theory. Experience with cloud computing platforms and GPU acceleration is a plus.
- Research and Publication: Track record of research contributions in generative AI, demonstrated through publications in top-tier conferences or journals. Active participation in the AI research community, such as attending conferences or workshops, is highly valued.
- Analytical and Problem-Solving Abilities: Strong analytical thinking and problem-solving skills to tackle complex challenges in generative AI. Ability to think creatively and propose innovative solutions. Attention to detail and the ability to analyze and interpret experimental results.
- Collaboration and Communication: Excellent teamwork and communication skills to effectively collaborate with cross-functional teams. Ability to explain complex technical concepts to both technical and non-technical stakeholders. Strong written and verbal communication skills.
- Adaptability and Learning: Enthusiasm for staying updated with the latest advancements in AI and generative models. Willingness to learn new techniques and adapt to evolving technologies and methodologies.
Join our team and contribute to the development of groundbreaking generative AI solutions. Together, we can push the boundaries of what is possible in the field.
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