Gen AI Lead

<p><strong>Role:</strong>&nbsp;GenAI Lead Engineer</p> <p><strong>Experience:</strong>&nbsp;8+ years total (with significant hands-on GenAI / LLM work)</p> <p><strong>Job Overview:</strong></p> <p>We are seeking an innovative and highly skilled&nbsp;<strong>Lead Generative AI (GenAI) Engineer</strong>&nbsp;to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.</p> <p><strong>Key Responsibilities:</strong></p> <ul> <li><strong>Technical Leadership:</strong></li> <li>Lead the end-to-end design and implementation of Generative AI solutions.</li> <li>Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.</li> <li>Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).</li> <li><strong>Solution Development:</strong></li> <li>Architect, train, and fine-tune state-of-the-art LLMs and generative AI models</li> <li>Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.</li> <li>Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform</li> <li>Ensure scalability, performance, and security of AI solutions deployed in production.</li> <li><strong>Integration and Deployment:</strong></li> <li>API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.</li> <li>Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.</li> <li>Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.</li> <li>Leverage MLOps practices for continuous model monitoring, retraining, and improvement.</li> <li><strong>Data Strategy and Preparation:</strong></li> <li>Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.</li> <li>Implement data governance and privacy practices in line with organizational policies.</li> <li><strong>Innovation and Research:</strong></li> <li>Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.</li> <li>Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.</li> <li><strong>Stakeholder Collaboration:</strong></li> <li>Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.</li> <li>Present technical concepts, project progress, and AI capabilities to non-technical audiences.</li> </ul> <p><strong>Key Requirements</strong></p> <ul> <li><strong>Technical Skills:</strong></li> <li>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.</li> <li>Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok</li> <li>Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi</li> <li>Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss</li> <li>Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)</li> <li>Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.</li> <li>Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.</li> <li>Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.</li> <li>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</li> <li>Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.</li> </ul> <p><strong>Qualifications:</strong></p> <ul> <li>Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).</li> <li>8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.</li> <li>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</li> <li>Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science</li> <li>Proven track record of leading AI projects from inception to production.</li> <li>Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).</li> <li>Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).</li> </ul> <p>&nbsp;</p>