About the Role
We’re seeking a Senior AI Engineer with extensive hands-on experience developing products powered by Large Language Models (LLMs) and Generative AI technologies. This role is ideal for an engineer who brings a depth of experience in machine learning, a strong full-stack background, and a product-driven mindset.
You’ll help lead the design and development of advanced AI systems—from building prototypes to scaling production systems. The ideal candidate is excited by the challenge of applying cutting-edge AI in meaningful, user-focused ways, and thrives at the intersection of R&D, engineering, and product.
What You’ll Do:
Architect and implement scalable AI systems and applications powered by LLMs and multi-agent frameworks.Lead end-to-end development efforts, including model integration, infrastructure design, and application logic.Prototype and deploy GenAI applications that combine retrieval, tool use, reasoning, and interactivity.Contribute to decision-making around model selection, finetuning, evaluation, and safety mechanisms.Monitor AI/ML performance in production and drive continuous improvement of prompt, RAG, and agent pipelines.Stay at the forefront of GenAI developments and bring innovative ideas into the product roadmap.,
What You Must Bring:
4+ years of experience working in ML or AI engineering roles, ideally with a focus on NLP or GenAI.Deep understanding of how modern LLMs work, including transformer architectures, finetuning, and evaluation.Hands-on experience implementing and optimizing GenAI techniques such as: Tool/function calling, Multi-agent workflows, Retrieval-Augmented Generation (RAG), Finetuning or custom training (e.g., LoRA, PEFT), and Structured prompting and evaluation. Proficiency with GenAI frameworks and tools (e.g., LangChain, LlamaIndex, Hugging Face, Haystack).Experience integrating LLMs into real-world applications, including building internal tooling or customer-facing AI features.Solid foundation in full-stack development or backend systems (Python, TypeScript, FastAPI, etc.).Experience designing and deploying scalable APIs and cloud infrastructure (AWS, GCP, or Azure).Proficient with databases (PostgreSQL, MongoDB, or vector DBs like Pinecone or Weaviate).Comfortable working in agile product teams and balancing experimentation with shipping reliable code.Strong Git/GitHub collaboration skills and comfort working with CI/CD workflows and containerization (Docker, etc.).,
Bonus Points:
Experience working in a startup or research-oriented environment.Prior exposure to open-source AI models (e.g., LLaMA, Mistral, Mixtral) and fine-tuning them.Publications, technical blog posts, or demos of past AI work.
