Key Responsibilities
Salaries: Negotiable
Applied Machine Learning & Engineering
• Design, build, and scale production-grade ML systems for personalization, recommendations, adaptive testing, and performance prediction.
• Architect and implement ML pipelines and services with CI/CD, monitoring, and model management.
• Ensure high availability, performance, and scalability of AI-driven components in the learning platform.
LLM/NLP Innovation
• Integrate and fine-tune large-scale LLMs (e.g., GPT-4, LLaMA, Claude) for conversational learning agents, automated feedback, summarization, and intelligent content generation.
• Experiment with prompt engineering, instruction tuning, and fine-tuning methods to optimize LLM behavior for educational contexts.
• Implement hybrid RAG systems with vector databases (e.g., Pinecone, Weaviate, Chroma) for contextual knowledge retrieval.
R&D and Thought Leadership
• Lead or co-lead AI research initiatives within the company to explore new techniques in generative AI, few-shot/fine-tuned models, adaptive learning algorithms, or NLP architectures.
• Evaluate emerging AI trends, tools, and methods; prototype new capabilities to inform the product roadmap.
• Collaborate with academia or research institutions for cutting-edge innovation and publications where applicable.
Collaboration & Mentoring
• Work cross-functionally with product, design, engineering, and content teams to align AI solutions with user and business needs.
• Mentor junior engineers, guide code reviews, and contribute to best practices across the AI/ML team.
Skills & Experience
• 5+ years of experience developing and deploying end-to-end machine learning solutions in production environments.
• Deep knowledge of Python and ML/NLP frameworks (e.g., PyTorch, TensorFlow, Hugging Face, scikit-learn).
• Proven experience with one or more LLMs (e.g., GPT, Claude, LLaMA) and tools such as LangChain, OpenAI API, Transformers.
• Strong grasp of ML fundamentals, NLP techniques, semantic search, and generative modeling.
• Experience with MLOps practices, including experiment tracking (MLflow/W&B), deployment (Docker/K8s), and cloud services (AWS/GCP/Azure).
• Experience leading or participating in applied research and rapid prototyping efforts.
• Bonus: Experience in speech-to-text, translation systems (ASR, S2ST), graph learning, or reinforcement learning.
Qualifications
• Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Demonstrated ability to bridge research and real-world applications in a fast-paced product environment.
• Strong communication, technical leadership, and collaboration skills.
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