Address • Technical Leadership: Lead a team of developers in the creation, implementation, and optimization of Generative AI solutions. Provide technical guidance, resolve challenges, and foster a collaborative environment. • Prompt Engineering: Spearhead the design and development of prompt engineering strategies to influence and control the output of Generative AI models. Optimize prompts for desired results. • Pipeline Design: Design end-to-end data pipelines that encompass data preprocessing, feature engineering, model training, and deployment. Ensure pipelines are efficient, scalable, and well-documented. • Technical Review: Review the technical outputs generated by the team, including code, models, and pipelines. Ensure high-quality and maintainable solutions that adhere to best practices. • Testing and Validation: Implement testing methodologies to validate the performance and accuracy of Generative AI models. Develop and execute unit tests, integration tests, and validation strategies. • Deployment Strategy: Collaborate with DevOps and deployment teams to deploy trained models into production environments. Ensure smooth integration and monitor performance post-deployment. • Workflow Optimization: Identify opportunities to optimize development workflows, enhance productivity, and streamline processes. Implement tools and practices to improve efficiency. • Collaboration: Interface with cross-functional teams, including data scientists, architects, and business stakeholders. Collaborate on solution design, implementation, and project milestones. • Documentation: Maintain comprehensive documentation of technical designs, code, and workflows. Ensure documentation is up-to-date, accessible, and understandable for team members.
• Machine learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks • Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras • Cloud computing platforms: AWS, Azure, GCP • Natural language processing (NLP): Transformer models, attention mechanisms, word embeddings • Computer vision: Convolutional neural networks, recurrent neural networks, object detection • Robotics: Reinforcement learning, motion planning, control systems • Data ethics: Bias in machine learning, fairness in algorithms • LLM Pipeline Creation: Strong experience in designing data pipelines, including data preprocessing, feature extraction, and model integration. Familiarity with best practices for creating efficient and scalable pipelines. • Leadership Skills: Proven leadership capabilities to guide and mentor a team of developers. Ability to provide technical direction, solve challenges, and inspire innovation within the team.
•Generative AI Expertise: Good understanding of various Generative AI techniques, including GANs, VAEs, and other relevant architectures. Proven experience in applying these techniques to real-world problems for tasks such as image and text generation. Conversant with Gen AI development tools like Prompt engineering, Langchain, Semantic Kernels, Function calling. Exposure to both API based and opens source LLMs based solution design. • Ability to develop value-creating strategies and models that enable clients to innovate, drive growth and increase their business profitability •Good knowledge on software configuration management systems • Awareness of latest technologies and Industry trends • Logical thinking and problem solving skills along with an ability to collaborate • Understanding of the financial processes for various types of projects and the various pricing models available • Ability to assess the current processes, identify improvement areas and suggest the technology solutions • One or two industry domain knowledge • Client Interfacing skills • Project and Team management
Artificial Intelligence->NLP Deep Learning Algorithms,Artificial Intelligence->Python ML,Analytics->Machine Learning->NLP-Speech Analytics
