Gen AI-Senior Technical Lead

Key Responsibilities - Technical Leadership: - Lead the end-to-end design and implementation of Generative AI solutions. - Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects. - Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs). - Solution Development: - Architect, train, and fine-tune state-of-the-art LLMs and generative AI models - Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning. - Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform - Ensure scalability, performance, and security of AI solutions deployed in production. - Integration and Deployment: - API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications. - Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services. - Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs. - Leverage MLOps practices for continuous model monitoring, retraining, and improvement. - Data Strategy and Preparation: - Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning. - Implement data governance and privacy practices in line with organizational policies. - Innovation and Research: - Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning. - Evaluate and recommend AI frameworks, libraries, and platforms for project requirements. - Stakeholder Collaboration: - Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases. - Present technical concepts, project progress, and AI capabilities to non-technical audiences. Key Requirements - Technical Skills: - Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI. - Hands on experience in any of LLMs such as OpenAI's ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok - Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi - Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss - Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF) - Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms. - Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models. - Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn. - Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important - Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker. Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred). - 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI. - Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution - Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science - Proven track record of leading AI projects from inception to production. - Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF). - Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning). Skills: Machine Learning, Devops, Database Management, Deep Learning, Api Development, data engineering , Cloud Computing, Big Data, Python Experience: 6.00-15.00 Years