Artificial Intelligence Engineer

Top 3-5 Must Have Skills for this role: - Strong skills in Python and data analysis libraries (Pandas, NumPy, SQL). - Demonstrable experience or strong projects in LLM/RAG development. - Strong proficiency in agentic LLM Libraries/Technologies like LangChain, LangGraph, AutoGen, CrewAI, etc. - Familiarity with RL, techniques for fine-tuning LLMs (e.g., LoRA), and other emerging ML methodologies. Qualifications • BS or MS Degree in Electrical Engineering, Computer Science/Engineering, or a related discipline (or equivalent experience). • 5+ years of proven industry experience • Skilled at rapidly taking products from concept to launch and scaling them massively by performance tuning and optimizing complex, globally distributed systems • Demonstrable experience or strong projects in LLM/RAG development. • Strong skills in Python and data analysis libraries (Pandas, NumPy, SQL). • Strong proficiency in agentic LLM Libraries/Technologies like LangChain, LangGraph, AutoGen, CrewAI, etc. • Familiarity with RL, techniques for fine-tuning LLMs (e.g., LoRA), and other emerging ML methodologies. • Optimize inference and infrastructure for low-latency, cost-effective operation (vLLM/TGI/Triton, batching, caching, quantization) on GPU/accelerators; support on-prem/VPC deployments with enterprise security controls. • A proactive approach to problem-solving and a willingness to acquire new skills and knowledge as needed to achieve results. Responsibility • Play key role in design, development, and deployment of large-scale high-performance enterprise ready agent frameworks and tools. • Collaborate with engineering team to understand specific needs and challenges of chip design and ensure our agent platform is well-suited to these needs • Develop and optimize retrieval and generation algorithms for enterprise data (text, code, and images) to build advanced AI applications. • Design, implement, test, and continuously optimize end-to-end RAG pipelines, including data parsing, ingestion, prompt engineering, and chunking strategies. • Collect & organize training / fine-tuning data and help build domain specific large language models. • Optimize infrastructure for performance, scalability, and reliability, and ensure secure and efficient management of data. • Stay ahead by engaging with the latest advancements in machine learning and AI to create state-of-the-art solutions.