[Hiring] GenAI Lead Engineer @Sparix Global.

Role Description

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.

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).

Requirements

• 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 databases 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.