Generative AI Lead

Company: Optimum Solutions

About the company: Optimum solutions is a leader in a sheet metal industry, provides sheet metal solutions to sheet metal fabricators with a proven track record of reliable product delivery. Starting from tools through software, machines, we are one stop shop for all your technology needs.

Position: Generative AI Lead

Location: Chennai (Preference) and Bangalore

Minimum Qualification:  Bachelor's degree in computer science, Software Engineering, Data Science, or a related field.

Experience:   10-12 years

CTC:  33LPA

Employment Type:  Full Time

Key Responsibilities:

1. Utilize advanced machine learning techniques to develop and train generative AI models.
2. Collaborate with cross-functional teams to groom and preprocess large datasets for model training.
3. Research and implement cutting-edge algorithms and architectures for generative AI applications.
4. Optimize model performance and scalability for real-time inference and deployment.
5. Stay current with industry trends and advancements in generative AI technology to drive innovation.
6. Experiment with different hyperparameters and model configurations to improve generative AI model quality.
7. Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation,
8. Choose suitable DL algorithms, software, hardware and suggest integration methods.
9. Ensure AI ML solutions are developed, and validations are performed in accordance with Responsible AI guidelines & Standards
10. To closely monitor the Model Performance and ensure Model Improvements are done post Project Delivery
11. Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.
12. Coach and mentor our team as we build scalable machine learning solutions
13. Strong communication skills and an easy-going attitude
14. Oversee development and implementation of assigned programs and guide teammates
15. Carry out testing procedures to ensure systems are running smoothly
16. Ensure that systems satisfy quality standards and procedures
17. Build and manage strong relationships with stakeholders and various teams internally and externally,
18. Provide direction and structure to assigned projects activities, establishing clear, precise goals, objectives and timeframes, run Project Governance calls with senior Stakeholders

Skills and Qualifications: 

1. Strong understanding of machine learning and deep learning principles and algorithms.
2. Experience in developing and implementing generative AI models and algorithms.
3. Proficiency in programming languages such as Python, TensorFlow, and PyTorch.
4. Ability to work with large datasets and knowledge of data preprocessing techniques.
5. Familiarity with natural language processing (NLP) and computer vision for generative AI applications.
6. Experience in building and deploying generative AI systems in real-world applications.
7. Strong problem-solving and critical thinking skills for complex AI problems.
8. Excellent communication and teamwork abilities to collaborate with cross-functional teams.
9. Proven track record of delivering innovative solutions using generative AI technologies.
10. Ability to stay updated with the latest advancements in generative AI and adapt to new techniques and methodologies.
11. Application Development- Framework , Langchain, LlamaIndex , Vector DB , Pinecone/Chroma/Weaviate
12. Fine Tuning Models-  Compute Service, Azure/Google Cloud/Lambda , Data Service, Scale/Labelbox, Hosting Services , Azure/AWS , ML Frameworks , Tensorflow,Pytorch
13. Model Hubs-  Hugging Face , Databricks , 
14. Foundation Models-  Open-Source , Mistral,Llama , Proprietary , GPT-4
15. Compute Hardware- Specialized hardware for model training and inference,  Specialized hardware for model training and inference
16. Programming Proficiency - 

• Advanced Python Skills - Generative AI Experts should have a deep understanding of Python, including its data structures, OOP’s concepts, and libraries such as NumPy and Pandas. They must be able to write clean, efficient, and maintainable code to implement complex AI algorithms.
• TensorFlow and Keras Expertise - TensorFlow and Keras are widely used in the AI community for building neural networks and deep learning models. Generative AI Experts should have a thorough understanding of these libraries, including how to design neural network architectures, customize loss functions, and optimize models for performance
• Debugging and Optimization - Solving complicated problems is a common part of developing generative AI models. Experts must be adept in debugging methods, such as logging and profiling data to find and address problems quickly. They should also know how to optimize code for memory efficiency and performance, which will help the models manage large-scale datasets
• Effective Data Management - One of the most frequent tasks in AI development is managing big datasets. Experts in generative AI should be adept at manipulating data with tools like Pandas and NumPy. To guarantee that the data they use for their models is of the highest caliber, they need also know how to efficiently preprocess and clean data.
• Version Control and Collaboration - Git and other version control systems are crucial for tracking code changes and fostering developer collaboration in a team environment. To enable smooth cooperation on AI projects, generative AI Experts should be familiar with Git workflows, branching techniques, and handling merge conflicts

  17. Deep Learning Expertise - Neural Networks , Convolutional Neural Network , Recurrent Neural Network

  18. Knowledge of Generative Models - Transformers and Attention networks , Generative adversarial network

  19.Generative AI Basics and Advanced Concepts

• Prompt Engineering - Crafting high-quality prompts is crucial for guiding generative models. Experts should excel in designing prompts that steer the model’s creativity and coherence. They must understand how to fine-tune prompts for tasks like text, image, and music generation.
• Attention Mechanisms - Grasping attention mechanisms in models like Transformers, vital for capturing dependencies and context in generative tasks.
• Application Development Approaches -Familiarity with integrating generative models into applications is essential. This includes deploying models in mobile apps, web applications, or as APIs. Experts should consider factors such as model size, latency, and scalability during deployment.
• Fine-Tuning - Mastery of techniques like fine-tuning language models (e.g., GPT-3) for specific tasks. This involves adjusting model parameters and prompts to generate contextually relevant and accurate outputs.
• RAG (Retrieval-Augmented Generation) - Understanding RAG, a framework that combines generative models with retrieval mechanisms. Experts can use RAG to improve model responses by retrieving relevant information from a large dataset. Proficiency in chaining multiple generative models together to create more complex and diverse outputs. This involves connecting models in a sequence to generate outputs that build upon each other.
• Multimodal Generation - Ability to generate outputs across multiple modalities (e.g., text and images), requiring integration of different generative models.