Lead AI Engineer

This role is for Weekday's client.

Role Overview

As the Lead AI Engineer , you will be responsible for spearheading the design, development, and deployment of AI solutions. You will work with various large language models (LLMs) both open-source and proprietaryoptimizing them through fine-tuning, prompt engineering, agentic frameworks, and retrieval-augmented generation (RAG) methodologies. Additionally, you will play a key role in managing the AI engineering team, fostering innovation, and ensuring successful execution of AI-driven projects.

Requirements

1. Lead the AI engineering team in designing, developing, and deploying LLM-powered solutions .
2. Work with open-source (Llama, Mistral, Falcon, etc.) and proprietary models (GPT-4, Claude, Gemini, etc.) to build state-of-the-art AI applications.
3. Develop strategies for fine-tuning models on proprietary datasets to enhance performance for specific use cases.
4. Architect and implement retrieval-augmented generation (RAG) systems for improved response accuracy and efficiency.
5. Build and integrate agentic frameworks that allow LLMs to autonomously reason, plan, and execute multi-step tasks.
6. Oversee the data pipeline, model training, and deployment workflows to ensure scalability and efficiency.
7. Collaborate with cross-functional teams (product managers, data scientists, and software engineers) to align AI development with business objectives.
8. Stay up to date with the latest advancements in AI research and bring innovative solutions to the company.
9. Ensure best practices for model evaluation, bias mitigation, and ethical AI deployment .
10. Drive the team's technical roadmap, hiring strategy, and mentorship initiatives .
    

1. 5+ years of experience in AI/ML engineering, with a strong focus on LLMs and NLP .
2. Proficiency in Python and AI frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, or similar .
3. Deep understanding of transformers, embeddings, tokenization, attention mechanisms, and distributed training .
4. Experience in fine-tuning large-scale models on domain-specific datasets.
5. Hands-on experience with vector databases (e.g., FAISS, Weaviate, Pinecone) for retrieval-based AI applications.
6. Strong knowledge of MLOps practices, including model deployment, monitoring, and lifecycle management.
7. Proven experience leading AI/ML teams, managing project timelines, sprints, and stakeholder expectations .
8. Experience with cloud platforms (AWS, GCP, Azure) and optimizing AI workloads for production environments.
9. Strong problem-solving skills with a research-driven mindset.
   

• Experience working with multi-modal models (text, image, video, audio).
• Knowledge of RLHF (Reinforcement Learning from Human Feedback) techniques.
• Contributions to open-source AI projects or published research papers.
• Prior experience in a high-growth AI startup or AI research lab .