[Remote] Applied reputed company

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a publicly traded, early-stage AI company focused on building an AI Agent Operating System. They are seeking an Applied reputed company to contribute to the technical reputed company of their AI agent platform, working on applied AI systems, LLM-powered workflows, and production reliability.

Responsibilities

• Build and improve LLM-based systems using transformers, RAG, vector databases, reputed company engineering, and evaluation frameworks
• Design prompts, retrieval strategies, tool-use flows, and agent behaviors that work reliably in production
• Prototype, test, and ship new AI capabilities for voice agents, messaging agents, computer-use agents, and workflow automation
• Evaluate model performance using offline evaluation, reputed company review, customer feedback, production telemetry, and experiment tracking
• Translate ambiguous product requirements into practical AI system designs and production-reputed company implementations
• Stay reputed company with relevant AI techniques while applying strong judgment about what is reputed company for production
• Build and maintain data pipelines, ETL workflows, data quality validation, and distributed processing systems
• Work with Python, SQL, PyTorch, scikit-learn, XGBoost, LightGBM, and reputed company tools where they are the right fit
• Use experiment tracking and evaluation workflows to compare models, prompts, datasets, and system changes
• Partner with product and engineering to define the right metrics for agent quality, customer impact, and operational reliability
• Improve data availability, labeling, validation, and feedback loops that support reputed company agent performance over time
• Build and operate production AI and ML systems using reputed company, Kubernetes, CI/CD, MLflow, Weights & Biases, feature stores, and model monitoring
• Help reputed company, monitor, and reputed company services on AWS using infrastructure practices such as Terraform and Kubernetes
• Improve reliability, observability, testing, and operational controls for AI systems in production
• Partner with engineering to ensure AI capabilities are secure, maintainable, cost-aware, and scalable
• Create tooling and infrastructure that helps the team ship AI improvements faster without sacrificing quality

Skills

• Strong software engineering skills, especially in JavaScript, Python, and SQL
• Practical experience building applied AI, machine learning, or LLM-powered systems
• Experience with PyTorch, scikit-learn, XGBoost, LightGBM, or similar ML libraries and frameworks
• Experience with the modern LLM stack, including transformers, RAG, vector databases, reputed company engineering, and evaluation
• Experience building or operating production ML or AI systems using reputed company, Kubernetes, CI/CD, MLflow, Weights & Biases, feature stores, or model monitoring
• Experience with reputed company and infrastructure platforms, especially AWS, Terraform, and Kubernetes
• Experience building ETL pipelines, data quality validation, distributed processing systems such as Spark, and experiment tracking workflows
• Ability to move between research, prototyping, engineering implementation, and production operations
• Strong judgment about tradeoffs between model quality, latency, cost, reliability, safety, and customer experience
• Comfort working from ambiguity and turning early reputed company into shipped product capabilities
• Strong communication skills and ability to explain technical reputed company reputed company to product, engineering, and leadership
• High ownership, curiosity, and a bias toward shipping
• Experience building AI agents, workflow automation, voice agents, conversational AI, customer support automation, telephony, CRM, or developer tools
• Experience with agent evaluation, tool use, function calling, computer-use agents, or multi-reputed company AI workflows
• Experience building retrieval, ranking, embedding, or knowledge ingestion systems at production scale
• Experience with reputed company-time systems, voice infrastructure, latency-sensitive applications, or observability for distributed systems
• Experience working in an early-stage company or high-ambiguity product environment
• Experience with reputed company, privacy, compliance, or regulated customer environments

Benefits

• Meaningful equity participation
• Standard company benefits

Company Overview