Senior Machine Learning Engineer

Team Overview

Outline of Duties and Responsibilities

• AI-Powered Data Collection Systems: Design and develop scalable AI-driven data collection and enrichment workflows across structured and unstructured data sources.
• LLM & Generative AI Workflows: Build LLM-based capabilities including RAG systems, prompt orchestration, entity extraction, summarization, classification, and automated validation workflows.
• Agentic Frameworks & Model Context Integration: Design and implement agentic workflows and model-to-tool integrations that connect AI models with internal tools, APIs, knowledge stores, data sources, and workflow systems.
• Model Deployment & Lifecycle Management: Deploy, maintain, and optimize ML and LLM models in production, including model versioning, CI/CD, experiment tracking, model registry, rollout strategies, and rollback mechanisms.
• Data Quality & Evaluation: Build frameworks for evaluating extraction quality, model performance, hallucination risks, grounding, consistency, latency, coverage, and overall data reliability.
• Observability & Operational Excellence: Implement monitoring, logging, tracing, alerting, cost tracking, model performance monitoring, drift detection, and reliability dashboards for production AI/ML systems.
• Scalable Platform Engineering: Design distributed, event-driven, and cloud-native systems using asynchronous processing, message queues, containerization, and orchestration patterns to support high-volume workloads.
• Innovation & Continuous Improvement: Evaluate emerging AI/ML technologies, LLM frameworks, orchestration tools, vector databases, and model deployment approaches to improve automation capabilities and developer productivity.
• Company Values: Model company values and contribute to a culture of innovation, accountability, collaboration, inclusion, and continuous improvement.

Experience, Skills and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical field.
• 5+ years of experience in machine learning engineering or data science, with a focus on machine learning systems, ML platforms, or distributed systems.
• Strong experience building production-grade ML systems, including model deployment and lifecycle management.
• Hands-on experience with MLOps tools and practices, including CI/CD, model monitoring, and experiment tracking.
• Strong programming skills in Python and SQL, or similar languages.
• Experience with cloud platforms and containerization (e.g., AWS/GCP/Azure, Docker, Kubernetes).
• Experience with LLM-based systems in production, including RAG pipelines, embeddings, and vector databases.
• Solid understanding of distributed systems, scalability, and system design trade-offs.
• Proven ability to solve complex technical challenges and deliver scalable solutions.
• Excellent communication and collaboration skills, with experience working across global teams.
• Experience working in fast-paced, data-driven environments.

Working Conditions