AI/ML Senior Engineer

•Deep Experience in LLMs: Strong understanding of LLMs, including their architecture, training process, and popular models (e.g., GPT-4, Claude, Gemini). •LLM Fine-tuning Techniques: Expertise in various LLM fine-tuning methods (e.g., SFT, PEFT, LoRA, DPO). •Prompt Engineering Expertise: Proven ability to design, evaluate, and optimize prompts for LLMs. •Frameworks and Tools: Experience with LLM frameworks (e.g., LangChain, LlamaIndex), MLOps tools (e.g., MLflow, Langsmith), and modern ML tooling (e.g., Hugging Face, PyTorch). •Problem-Solving Skills: Excellent problem-solving skills with the ability to identify and resolve complex technical challenges. •LLM Fine-tuning and Adaptation: •Develop and implement strategies for fine-tuning pre-trained LLMs on custom datasets to improve performance and tailor them to specific tasks or domains. •Employ techniques like parameter-efficient fine-tuning (PEFT), supervised fine-tuning, LoRA, and DPO to efficiently adapt LLMs. •Design and conduct experiments to fine-tune and evaluate LLM-based agents, iterating to improve performance and reliability. •LLM Fine-tuning and Adaptation: •Develop and implement strategies for fine-tuning pre-trained LLMs on custom datasets to improve performance and tailor them to specific tasks or domains. •Employ techniques like parameter-efficient fine-tuning (PEFT), supervised fine-tuning, LoRA, and DPO to efficiently adapt LLMs. •Design and conduct experiments to fine-tune and evaluate LLM-based agents, iterating to improve performance and reliability. •Prompt Engineering and Optimization: •Design, test, and scale effective prompts to elicit desired responses and behaviors from LLMs. •Optimize prompt strategies for planning and decision-making in LLM-driven applications. •Evaluate the efficiency of LLM prompts to create flexible, impactful, cost-effective solutions. •Improve products and customer experiences through A/B testing on applied LLM components. •LLM Integration and Application Development: •Design and build orchestration infrastructure for LLM-based agents. •Integrate with third-party LLM APIs (e.g., OpenAI, Claude) and open-source models (e.g., Mistral, LLaMA). •Implement LLM-powered applications using frameworks like LangChain and LlamaIndex. •Develop AI-driven features, including chat-based interfaces that interact with products on behalf of users. •Model Optimization and Deployment: •Apply mod el optimization techniques such as pruning and quantization to improve on-device performance and reduce latency. •Ship production-grade software in complex system environments. •Leverage cloud platforms (AWS, GCP, Azure, OpenShift) for deploying and scaling LLM applications. •Ensure high availability, fault tolerance, and security in application architecture. •Collaboration and Technical Leadership: •Collaborate closely with modeling and UX teams to define behavior, context handoff, and interaction patterns across modalities and form factors. •Evangelize LLM operations to different teams across the organization. •Provide technical guidance and direction in the domain of LLM fine-tuning and prompt engineering.

Salary Range: $120,000-$180,000 a year