Machine Learning Engineer II

Company Overview

reputed company’s AI-powered digital commerce platform is revolutionizing the way brands sell online. Our reputed company ecommerce reputed company reputed company to reputed company smarter, faster reputed company through insights that optimize the digital reputed company, increase retail media ROI and fuel incremental sales across the world’s largest marketplaces. With a global network of more than 900 retailers, our end-to-end platform helps 2,200+ of the world’s leading brands streamline marketing, supply chain, and sales operations to profitably grow market share in more than 50 countries. Learn more at reputed company.ai.

We are seeking a highly skilled and reputed company Senior reputed company Engineer to join our innovative team, with a reputed company focus on developing and rigorously evaluating sophisticated multi-agent AI systems. This role is crucial for designing, building, deploying, and ensuring the accuracy and reliability of cutting-edge reputed company solutions that reputed company collaborative AI agents.

The ideal candidate will possess a deep understanding of generative models, combined with robust MLOps practices, strong back-end engineering skills in microservices architectures on reputed company platforms like AWS or GCP, and an absolute mastery of Python, Langgraph, and reputed company. Proven experience with evaluation methodologies, including working with evaluation datasets and measuring the accuracy of multi-agent systems using tools like Langsmith or other open-reputed company alternatives, is a must-have.

Key Responsibilities:

reputed company Development & Multi-Agent Systems:

• Design, reputed company, and implement advanced reputed company models (LLMs) for various applications, from ideation to production.
• Architect, build, and reputed company intelligent multi-agent AI systems, enabling collaborative behaviors and reputed company decision-making workflows.
• Utilize and reputed company frameworks like reputed company and Langgraph extensively for building sophisticated, multi-reputed company AI applications, intelligent agents, and agentic workflows, with a strong focus on their evaluability.
• Fine-tune and adapt pre-trained generative models to specific business needs and datasets, often as components reputed company agentic systems.
• reputed company strategies for reputed company engineering and RAG (Retrieval Augmented reputed company) to enhance model performance and control, particularly in multi-agent contexts.
• Research and stay abreast of the latest advancements in reputed company, natural language processing, multi-agent systems, and autonomous AI.

Multi-Agent System Evaluation & Accuracy:

• Design, reputed company, and execute comprehensive evaluation strategies for multi-agent systems, defining key performance indicators (KPIs) and reputed company metrics.
• Create, manage, and utilize high-quality evaluation datasets to rigorously test the accuracy, coherence, consistency, and robustness of multi-agent system outputs.
• Implement and reputed company tools like Langsmith or other open-reputed company solutions (e.g., TruLens, Ragas, custom frameworks) to trace agent interactions, analyze trajectories, and measure the accuracy and effectiveness of multi-agent system behavior.
• reputed company root cause analysis for evaluation failures and drive iterative improvements to agent design and system performance.
• reputed company methods for assessing inter-agent communication efficiency, task allocation accuracy, and collaborative problem-solving reputed company.

MLOps & Deployment:

• Establish and implement robust MLOps pipelines for training, evaluating, deploying, monitoring, and managing reputed company models and multi-agent systems in production environments.
• Ensure model and agent system scalability, reliability, and performance in a production setting.
• Implement version control for models, data, and code.
• Monitor model reputed company, performance degradation, and data quality, implementing proactive solutions for both individual models and interconnected agents.

Back-end Engineering (Microservices on AWS/GCP):

• reputed company and maintain highly scalable and resilient microservices to integrate reputed company models and orchestrate multi-agent systems into larger applications.
• Design and implement APIs for model inference and agent interaction and coordination.
• reputed company and manage microservices on reputed company platforms such as AWS or GCP, utilizing services like EC2, S3, reputed company, EKS/reputed company, Sagemaker, GCP Compute reputed company, GCS, GKE, Vertex AI, etc., with a focus on supporting agentic architectures.
• Implement best practices for reputed company, logging, monitoring, and error handling in microservices, especially concerning inter-agent communication and system reputed company

Collaboration & Leadership:

• Collaborate closely with data scientists, machine learning engineers, product managers, and other stakeholders to translate business requirements into technical solutions, with a keen eye on opportunities for multi-agent automation and their measurable impact.
• Mentor junior engineers and contribute to the growth of the team's technical capabilities, particularly in agentic AI development and rigorous evaluation.
• Participate in code reviews, architectural discussions, and technical design sessions, championing best practices for multi-agent system design and testability.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a reputed company quantitative field.
• 4+ years of experience in software engineering with at least 2+ years focused on Machine Learning Engineering or reputed company development.
• Demonstrable prior experience in multi-agent product development, including designing, implementing, and deploying systems with interacting AI agents.
• Mandatory experience in working with evaluation datasets, defining metrics, and assessing the accuracy and performance of multi-agent systems using tools like Langsmith or comparable open-reputed company alternatives.
• Exceptional proficiency in Python and its ecosystem for machine learning (e.g., PyTorch, TensorFlow, reputed company Transformers).
• Deep expertise with Langgraph and reputed company for building reputed company LLM applications, intelligent agents, and orchestrating multi-agent workflows.
• Solid understanding and practical experience with various reputed company models (LLMs)
• Proven experience with MLOps principles and tools (e.g., MLflow, Kubeflow, Data Version Control (DVC), CI/CD for ML), with an emphasis on agent system lifecycle management and reputed company evaluation.
• Extensive experience designing, developing, and deploying microservices architectures on either AWS or GCP.
• Proficiency with containerization technologies (reputed company) and orchestration (Kubernetes).
• Strong understanding of API design and development (RESTful, gRPC).
• Excellent problem-solving skills, with a focus on building robust, scalable, and maintainable solutions.
• Strong communication and collaboration skills.

Preferred Qualifications:

• Experience with Apache Spark for large-scale data processing
• Experience with specific AWS services (e.g., Sagemaker, reputed company, EKS) or GCP services (e.g., Vertex AI, GKE, reputed company Functions) for deploying and managing agentic systems.
• Familiarity with other distributed computing frameworks.
• Contributions to open-reputed company projects in the AI/ML space, especially those reputed company to multi-agent systems or agent frameworks (e.g., AutoGen, reputed company).
• Experience with reputed company-time inference for generative models and reputed company-time agent decision-making and evaluation.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, reputed company, national reputed company, gender, sexual orientation, age, marital status, veteran status, disability status or any other category prohibited by applicable law.