Social Good & Community Development
Full-Time
Ekloud, Inc.
Overview
About The Role
We are seeking a highly skilled and forward-thinking Lead Data Scientist to join our cutting-edge AI/ML team. This role demands deep expertise in Generative AI, LLMs, and end-to-end ML engineering, with a strong grasp of scalable systems, modern architectures, and AI deployment in production environments.
You will be at the forefront of designing intelligent systems using large language models (LLMs), advanced NLP techniques, and modern ML practices. If youre passionate about pushing the boundaries of what's possible with GenAI and delivering real-world AI solutions, wed love to talk.
Key Responsibilities
We are seeking a highly skilled and forward-thinking Lead Data Scientist to join our cutting-edge AI/ML team. This role demands deep expertise in Generative AI, LLMs, and end-to-end ML engineering, with a strong grasp of scalable systems, modern architectures, and AI deployment in production environments.
You will be at the forefront of designing intelligent systems using large language models (LLMs), advanced NLP techniques, and modern ML practices. If youre passionate about pushing the boundaries of what's possible with GenAI and delivering real-world AI solutions, wed love to talk.
Key Responsibilities
- Lead the design and development of GenAI solutions using LLMs, NLP, and deep learning models.
- Build and optimize production-grade ML pipelines and AI systems across various domains.
- Design and deploy RAG (Retrieval-Augmented Generation) architectures and intelligent chatbots.
- Work with cross-functional teams to integrate AI components into scalable applications.
- Provide technical leadership, conduct code reviews, and mentor junior team members.
- Drive experimentation with prompt engineering, agentic workflows, and domain-driven designs (DDD).
- Ensure best practices in testing (TDD), clean architecture (Hexagonal), and model reproducibility.
- AI/ML Expertise : Machine Learning, NLP, Deep Learning, Generative AI (GenAI)
- LLM Stack : GPT, Chatbots, Prompt Engineering, RAG
- Programming : Python, including Pandas, NumPy, Scikit-learn, XGBoost
- Architecture : Agentic AI, DDD, TDD, Hexagonal Architecture
- Tooling & Deployment : Terraform, Docker, REST/gRPC APIs, Git
- Cloud Platforms : AWS / GCP / Azure
- AI Tooling : Familiarity with Copilot, Tabnine, or other AI-assisted coding tools
- Distributed Training : Hands-on with NVIDIA GPU-enabled environments for model training
- End-to-End Ownership : Proven experience managing the full ML lifecycle from experimentation to deployment
- Experience with vector databases (e.g., FAISS, Pinecone)
- Knowledge of LangChain, LlamaIndex, or similar GenAI frameworks
- Contribution to open-source GenAI/ML projects
- Experience in performance tuning of LLMs and fine-tuning custom models
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