KANINI
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.
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.