AI Engineer (Remote)

Must-Have Qualifications:
1. STEM Background: Minimum 4 year experiences with a Bachelor's or Master's with minimum 3 year experience in a STEM field (e.g., Computer Science, Engineering, Physics, Mathematics) with a dedicated focus on AI/ML in the last 1 2 years.
2. Proven experience with Google Gemini / Vertex AI (Non-negotiable). Hands-on experience with Gemini 2.5 family of generative AI models and associated technologies, including training, fine-tuning, and deployment.
3. Languages: Proficiency in Python, TensorFlow and other Machine Learning and Artificial Intelligence libraries for building LLM agents, tools and MCP servers.
4. Context Engineering: Proven experience performing context engineering and prompt tunning.
5. Create REST-APIs to serve AI services and Front-end development for user interface design and development.
6. Experience with Vector databases such as Pgvector, AlloyDB etc., graph databases, and other knowledge representation technologies. Clear understanding of Retrieval Augmented Generation (RAG), knowledge representation techniques.
7. Unstructured Data Handling: Demonstrated ability to work with unstructured data, including text documents, images, and other media types, and perform data cleaning, preprocessing, and transformation for AI applications (Non-negotiable).

Responsibilities:
1. Perform RAG, Grounding, Prompt Engineering in a project: Craft and refine effective prompts to achieve optimal results in various tasks related to product development.
2. Excellent attention to detail and organizational skills. Strong programming skills including in python, ML ecosystem tools, and generative AI.
3. Knowledge + Vector Database Development: Design, develop, and implement a robust Vector Database using LLMs and other AI technologies, focusing on capturing information from diverse sources (PDFs, design documents, regulatory requirements, etc.).
4. Data Extraction and Structuring: Develop and implement pipelines for extracting data from unstructured documents, including tables, text, and images, structuring the data for LLM training and usage.
5. LLM Fine-Tuning and Training: Fine-tune and train generative AI models on Client's specific engineering data and domain knowledge to create domain-specific models.
6. AI Tool Development: Design and develop a user-friendly GenAI tool that integrates with the knowledge base, allowing engineers to ask questions, generate technical requirements, and perform engineering analyses.
7. Technical Requirements Generation: Implement functionality for automatically generating technical requirements from design documents, user stories, and system requirements using LLMs.
8. Documentation and Knowledge Transfer: Thoroughly document all development processes, including designs, architecture, code, and training procedures. Provide adequate knowledge transfer to Client's internal teams, enabling them to maintain and expand the system.
9. Collaboration: Work closely with the Subject Matter Experts and engineers to implement and optimize AI-powered workflows.

Additional Preferred Skills (not required, but a plus):
Experience in creating REST-APIs.
Familiarity with medical device development regulations (e.g., FDA guidelines, ISO 13485).
Experience with front-end development for user interface design and development.

Required Skills
Full Stack Engineering: Experience building full stack application for service AI service.
Generative AI & LLM Platforms: 2+ years of experience with RAG, Grounding, Tuning, prompting using Gemini Vertex AI or Bing chat at enterprise level. Python, Java, C++, C#) for building LLM agents and tools.
Agent Building: Experience building AI/LLM agents (depending on the project's specific needs) using Python and Go for building LLM agents and tools. Experience Building MCP servers. Semantic analysis with Product Requirement text; technical use case, test cases etc.

Preferred Skills
Experience in creating REST-APIs.
Experience with medical device development processes
Experience building complex applications with the OpenAI API or other LLMs

Education Requirements:
Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years.

Software Skills Required:
Python, Java, C++, C#) for building LLM agents and tools. Software Version Control: (e.g., Git, SVN) for collaborative development.
Testing and Debugging Tools: (e.g., JUnit, pytest) for ensuring the quality of code and LLM agents.
Cloud computing platforms: Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform is beneficial for working with large language models.
Version control systems: Git for collaboration and tracking code changes (optional).

Required Certifications
Consider including certifications specific to AI/ML, such as Certified AI Professional (CAIP) or related certifications in the area of LLM technology.