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).

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