Description
Role: GenAI Lead Engineer
Experience: 8+ years total (with significant hands-on GenAI / LLM work)
Job Overview:
We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer 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.
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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