Maino.ai - ML Engineer (Data Scientist)

About Maino.ai

Maino.ai is at the forefront of revolutionising marketing automation through its end-to-end automated, intelligent, and ROI-driven martech platform. Leveraging cutting-edge Machine Learning (ML) and Generative AI technologies, we are committed to providing a one-stop solution that addresses all the marketing needs of organisations. Our mission is to build the world's most reliable and innovative technology platform, transforming the digital marketing sector into a cost-efficient and accessible domain for everyone.

Job Description:

At Maino.ai, we are seeking talented folks with 5+ years of experience in AI/ML with comprehensive understanding of the ML models ecosystem. This role offers a unique opportunity for someone passionate about pushing the boundaries of technology and thriving in a dynamic environment.

Qualifications:

Must-Have:

● Proficiency in Python and libraries like TensorFlow, PyTorch, Transformers, and LangChain.

● Solid understanding of NLP techniques, including tokenization, embeddings, and model fine-tuning.

● Hands-on experience with designing APIs and integrating AI models into production systems.

● Strong knowledge of machine learning fundamentals and evaluation metrics for conversational AI.

● Excellent problem-solving skills with a focus on delivering scalable and maintainable solutions.

● Strong experience with Large Language Models (LLMs) and frameworks such as OpenAI APIs, Hugging Face, or similar tools. (At least 1 year development experience)

Good-to-Have:

● Experience in deploying ML models in cloud environments (AWS, Azure, GCP).

● Exposure to tools like Docker, Kubernetes, or other container orchestration systems.

● Prior experience in developing multimodal conversational systems.

Responsibilities:

● Implement scalable data pipelines for processing and indexing large corpora of documents for retrieval systems.

● Research and apply advanced techniques in prompt engineering, few-shot learning, and knowledge integration to improve conversational and retrieval quality.

● Collaborate with other engineering teams.

● Conduct rigorous A/B testing, monitor key performance indicators (KPIs), and iteratively optimise system performance.

● Stay updated with the latest advancements in natural language processing (NLP) and machine learning to continuously innovate solutions.

● Ensure data privacy, ethical AI principles, and compliance with security standards in all models and systems.