AI Engineer

Model Development & Ownership: Design, develop, and optimize robust RAG (Retrieval-Augmented

Generation) pipelines to facilitate effective information retrieval in conversational AI systems.

AI Agent Deployment: Build and deploy intelligent AI agents capable of handling multi-turn

conversations, executing autonomous tasks, and managing tool invocation based on user intent.

System Integration: Implement and maintain a Model Context Protocol to ensure seamless,

standardized connectivity and data sharing between our AI systems, external APIs, and databases.

Data Architecture: Manage and optimize vector databases and advanced indexing techniques to

efficiently store and retrieve contextual data.

LLM Optimization: Fine-tune large language models (LLMs) and apply advanced prompt engineering

techniques to optimize for specific product use cases.

NLP/NLU Implementation: Implement powerful NLP, NLU, and NLG techniques (sentiment analysis,

entity recognition, text generation) to enhance product capabilities.

Continuous Improvement: Stay abreast of the latest ML and AI advancements, proactively suggesting

new tools or frameworks to improve our tech stack.

Cross-Functional Collaboration: Partner with product and engineering teams to translate AI capabilities

into practical, scalable applications.

Requirements:

Degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

3+ years of industry experience in machine learning engineering, with a strong focus on building and

deploying conversational AI or NLP solutions.

Proven, hands-on experience developing RAG models, working with vector databases (e.g., Pinecone,

Milvus, Weaviate), prompt engineering, and utilizing LLMs.

Strong, production-level programming skills in Python and proficiency with ML frameworks such as

PyTorch, TensorFlow, or JAX.