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Staff Machine Learning Engineer - Agentic Models, LLM, RAG, GenAI
Eightfold AI
Santa Clara, USA
Full-time
Senior · 6+ years experience
Hybrid
USD 232000-310000 yearly / year
Discovered 1 weeks ago
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- Lead the team in: research, design, development, and deployment of advanced AI agents and agentic systems.
- Architect and implement complex multi-agent systems, including planning, decision-making, and execution capabilities.
- Develop and integrate large language models (LLMs) and other state-of-the-art AI techniques to enhance agent autonomy and intelligence.
- Build robust, scalable, and reliable infrastructure to support the deployment and operation of AI agents at scale.
- Diagnose and troubleshoot issues in complex distributed environments and optimize system performance.
- Contribute to the team's technical growth and knowledge sharing.
- Stay up-to-date with the latest advancements in AI research and agentic AI and apply them to our products.
- Leverage enterprise data, market data, and user interactions to build intelligent and personalized agent experiences.
- Knowledge and passion in machine learning algorithms, Gen AI, LLMs, and natural language processing (NLP).
- Understanding of agent-based modeling, reinforcement learning, and autonomous systems.
- Experience with large language models (LLMs) and their applications in Agentic AI.
- Proficiency in programming languages such as Python, and experience with machine learning frameworks like TensorFlow or PyTorch.
- Experience with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes).
- Understanding of distributed system design patterns and microservices architecture.
- Experience with message queuing systems (AWS SQS, Kafka).
- Hands-on experience with system integration patterns and API design.
- Excellent problem-solving and data analysis skills.
- Strong communication and collaboration skills.
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related field, or equivalent years of experience.
- Min 6-10-+ years of relevant work experience in AI, Machine Learning, and applying data science to real-world use cases.
- Strong track record of taking systems from prototype to production with a focus on scalability and reliability.
- Experience with RAG architectures, including hybrid retrieval, vector databases (Pinecone, pgvector), and rerankers.
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