Job Description:
At Maino.ai, we are seeking talented folks with 2-3 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:- Strong experience with Large Language Models (LLMs) and frameworks such as OpenAI APIs, Hugging Face, or similar tools. (At least 1 year development experience)
- Expertise in building retrieval systems, including vector search techniques using FAISS, Pinecone, or Elasticsearch.
- 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.
Good-to-Have:- Experience in deploying ML models in cloud environments (AWS, Azure, GCP).
- Familiarity with knowledge graphs, ontologies, and other knowledge management systems.
- Exposure to tools like Docker, Kubernetes, or other container orchestration systems.
- Knowledge of reinforcement learning techniques (e.g., RLHF) for chatbot optimization.
- Prior experience in developing multimodal conversational systems.
Responsibilities:
- Design, develop, and deploy chatbot solutions leveraging state-of-the-art LLMs (e.g., OpenAI GPT, Cohere, Anthropic Claude).
- Build RAG pipelines by integrating LLMs with retrieval systems such as vector databases (e.g., Pinecone, Weaviate, or FAISS).
- Fine-tune pre-trained LLMs on domain-specific datasets for improved task performance and alignment with business goals.
- 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 engineering teams to integrate chatbot and RAG systems into production environments.
- 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.
