Lead AI Engineer

This role is for Weekday's client.
\n 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 proprietary—optimizing 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

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1. Lead the AI engineering team in designing, developing, and deploying LLM-powered solutions .
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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.
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3. Develop strategies for fine-tuning models on proprietary datasets to enhance performance for specific use cases.
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4. Architect and implement retrieval-augmented generation (RAG) systems for improved response accuracy and efficiency.
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5. Build and integrate agentic frameworks that allow LLMs to autonomously reason, plan, and execute multi-step tasks.
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6. Oversee the data pipeline, model training, and deployment workflows to ensure scalability and efficiency.
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7. Collaborate with cross-functional teams (product managers, data scientists, and software engineers) to align AI development with business objectives.
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8. Stay up to date with the latest advancements in AI research and bring innovative solutions to the company.
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9. Ensure best practices for model evaluation, bias mitigation, and ethical AI deployment .
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10. Drive the team's technical roadmap, hiring strategy, and mentorship initiatives .
    
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1. 5+ years of experience in AI/ML engineering, with a strong focus on LLMs and NLP .
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2. Proficiency in Python and AI frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, or similar .
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3. Deep understanding of transformers, embeddings, tokenization, attention mechanisms, and distributed training .
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4. Experience in fine-tuning large-scale models on domain-specific datasets.
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5. Hands-on experience with vector databases (e.g., FAISS, Weaviate, Pinecone) for retrieval-based AI applications.
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6. Strong knowledge of MLOps practices, including model deployment, monitoring, and lifecycle management.
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7. Proven experience leading AI/ML teams, managing project timelines, sprints, and stakeholder expectations .
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8. Experience with cloud platforms (AWS, GCP, Azure) and optimizing AI workloads for production environments.
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9. Strong problem-solving skills with a research-driven mindset.
   
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• Experience working with multi-modal models (text, image, video, audio).
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• Knowledge of RLHF (Reinforcement Learning from Human Feedback) techniques.
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• Contributions to open-source AI projects or published research papers.
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• Prior experience in a high-growth AI startup or AI research lab .
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