AI/ML Engineer
This is a remote position.
Job Summary:
· We are looking for highly skilled AI Engineers with expertise in Large Language Models (LLMs) and hands-on experience with advanced AI platforms such as Gemini 2.5, GitHub Copilot, Glean, and other generative AI tools.
· The ideal candidate will be able to design, develop, and deploy AI-powered solutions that enhance productivity, accelerate workflows, and drive innovation.
· Healthcare industry experience is a strong plus.
Key Responsibilities:
- Design and implement AI-driven solutions leveraging Gemini 2.5, Copilot, Glean, and other LLM-based platforms.
- Integrate generative AI tools into enterprise workflows to improve knowledge management, search, and automation.
- Collaborate with product, engineering, and data science teams to build scalable AI applications.
- Fine-tune and optimize AI/LLM models for specific business and healthcare use cases.
- Conduct experiments, perform benchmarking, and ensure high-quality AI deliverables.
- Ensure compliance, security, and ethical usage of AI in production.
- Stay up to date with the latest advancements in AI/LLMs, RAG, knowledge graphs, and healthcare AI trends.
Required Skills & Experience:
- Proven hands-on experience with Gemini 2.5, GitHub Copilot, Glean, or similar LLM-based tools.
- Solid understanding of LLM architectures, embeddings, and retrieval-augmented generation (RAG).
- Proficiency in Python, JavaScript/TypeScript, or similar programming languages.
- Experience with cloud platforms (AWS, Azure, GCP) and AI/ML deployments.
- Familiarity with Vector Databases (Pinecone, Weaviate, FAISS, Milvus, etc.).
- Knowledge of ML pipelines, APIs, and prompt engineering.
- Excellent problem-solving, communication, and collaboration skills.
Preferred Qualifications:
- Healthcare industry background or prior work on AI/ML healthcare projects (strong plus).
- Experience with enterprise search systems and AI-driven knowledge management tools.
- Exposure to LangChain, LlamaIndex, Hugging Face, OpenAI API, or similar frameworks.
- Hands-on with AI copilots for development, clinical data processing, or decision support.
- Strong understanding of data privacy and compliance (HIPAA, PHI handling, etc.) in healthcare.