Machine Learning Engineer

• AI-Powered Data Collection Systems: Develop and support scalable AI-driven data collection and enrichment workflows across structured and unstructured data sources.
• LLM & Generative AI Workflows: Build and maintain LLM-based capabilities including RAG systems, prompt orchestration, entity extraction, summarization, classification, and automated validation workflows.
• Agentic Frameworks & Model Context Integration: Contribute to 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: Support deployment, maintenance, and optimization of ML and LLM models in production, including model versioning, CI/CD, experiment tracking, model registry, rollout strategies, and rollback mechanisms.
• Data Quality & Evaluation: Implement evaluation frameworks for extraction quality, model performance, hallucination risks, grounding, consistency, latency, coverage, and overall data reliability.
• Observability & Operational Excellence: Build and maintain monitoring, logging, tracing, alerting, cost tracking, model performance monitoring, drift detection, and reliability dashboards for production AI/ML systems.
• Scalable Platform Engineering: Develop 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 and apply 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.
• 3+ years of experience in machine learning engineering, data science, software engineering, ML platforms, or distributed systems.
• Experience building, deploying, or maintaining production ML systems, including model deployment, inference services, or lifecycle management.
• Hands-on experience with MLOps tools and practices, including CI/CD, model monitoring, experiment tracking, automated testing, or deployment automation.
• Strong programming skills in Python and SQL, or similar languages.
• Experience with cloud platforms and containerization technologies such as AWS, GCP, Azure, Docker, or Kubernetes.
• Experience with LLM-based systems or related capabilities, including RAG pipelines, embeddings, vector databases, prompt orchestration, or model evaluation.
• Understanding of distributed systems, scalability, data pipelines, and system design trade-offs.
• Ability to solve technical challenges and deliver reliable, maintainable, and scalable solutions.
• Strong communication and collaboration skills, with experience working across product, engineering, data, or business teams.
  • Experience working in fast-paced, data-driven environments.