Gen AI Lead

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).