As an AI Engineer, you will be responsible for building and optimizing AI-first solutions that power BotPenguin's conversational and Agentic capabilities. You will work on LLM integrations, NLP pipelines, and machine learning models, while collaborating with cross-functional teams to deliver intelligent experiences at scale.

This is a high-impact role that combines engineering, research, and deployment skills to solve real-world problems using artificial intelligence.

What you need for this role

Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline.

Experience: 2–5 years of experience working in AI/ML or related software engineering roles.

Technical Skills:

Strong proficiency in Python and libraries such as scikit-learn, PyTorch, TensorFlow, Transformers (Hugging Face).

Hands-on experience with LLMs (OpenAI, Claude, LLaMA) and building AI agents using API integrations.

Experience working with NLP tasks (intent classification, text generation, embeddings, summarization).

Familiarity with Vector Databases like Pinecone, FAISS, Elastic Vector DB.

Understanding of Prompt Engineering, RAG (Retrieval-Augmented Generation), and embedding generation.

Proficiency in building and deploying ML models via Docker/Kubernetes or cloud services like AWS/GCP.

Experience with version control systems (GitLab/GitHub) and working in Agile teams.

Soft Skills:

Strong analytical thinking and problem-solving capabilities.

Passion for research, innovation, and applying AI to real-world use-cases.

Excellent communication skills and the ability to collaborate across departments.

Attention to detail with a focus on model accuracy, explainability, and performance.

What you will be doing

Design, build, and optimize AI-powered chatbot features and virtual agents using state-of-the-art models.

Collaborate with the Product, Backend, and UI teams to integrate intelligent workflows into the BotPenguin platform.

Build, evaluate, and fine-tune language models and NLP components tailored to user use-cases.

Implement context-aware chat solutions using embeddings, vector stores, and retrieval mechanisms.

Create internal tools for prompt testing, versioning, and debugging AI responses.

Monitor model performance metrics such as latency, hallucination rate, and user satisfaction.

Explore research papers, open-source innovations, and contribute to rapid experimentation.

Write clean, modular, and testable code along with clear documentation for future scalability.

Any other development related tasks as required for BotPenguin.

Guiding, reviewing the code written by junior members in the team.