Responsibilities
- Architect and design end-to-end generative AI solutions (text, image, audio, or multimodal) that align with business objectives.
- Evaluate and select appropriate foundation models (e.g., GPT, LLaMA, Stable Diffusion) and fine-tuning strategies.
- Lead the development of custom LLM applications, including prompt engineering, fine-tuning, RLHF, and model compression.
- Collaborate with cross-functional teams (engineering, product, design, data science) to integrate AI into products and platforms.
- Ensure responsible and ethical AI practices are embedded in system design (e.g., fairness, privacy, explainability).
- Guide the implementation of AI infrastructure (data pipelines, vector databases, model serving, APIs).
- Stay up-to-date on the latest AI research and tools, and make recommendations for adoption.
- Conduct proofs-of-concept, prototypes, and performance benchmarking.
- Mentor junior engineers and contribute to best practices and internal knowledge sharing.
Basic qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning
- 7+ years of experience in AI/ML, with 3+ years in generative AI (LLMs, diffusion models, etc.).
- Proven experience designing and deploying large-scale AI systems.
- Deep understanding of transformer architectures, tokenization, and pretraining/fine-tuning paradigms.
- Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, etc.
- Strong knowledge of MLOps, cloud platforms (AWS, GCP, Azure), and scalable architectures (e.g., microservices, serverless).
- Experience with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) systems.
- Familiarity with responsible AI frameworks and privacy-preserving techniques.
Preferred qualifications
- Experience with open-source LLMs and model distillation/quantization techniques.
- Exposure to multimodal AI models (e.g., CLIP, DALL·E, Imagen).
- Contributions to AI/ML research (e.g., published papers, open-source projects).
- Experience building GenAI copilots, chatbots, or productivity tools.
- Strong problem-solving and analytical skills.
- Excellent communication and stakeholder management abilities.
- Ability to translate complex AI concepts into business value.
- Entrepreneurial mindset and passion for innovation.
Benefits
- Dental insurance
- Health insurance
- Vision insurance
Tags & focus areas
Used for matching and alerts on DevFound Contract Remote Ai Machine Learning Data Science Mlops Generative Ai Pytorch Tensorflow