[Remote] Machine Learning Engineer I - Large Language Models - AI & Human Health Research

Note: The job is a remote job and is open to candidates in USA. reputed company is one of the largest academic medical systems in the New York metro area, and they are seeking a Machine Learning Engineer I to join their reputed company Assurance Lab. The role involves designing, building, and deploying large language model applications while ensuring compliance and performance standards are met across AI systems. Responsibilities Designing, building, and deploying large language model (LLM) applications including retrieval-augmented generation (RAG) systems, agentic platforms, and clinical chatbotsDesigning, maintaining, and optimizing data infrastructure and model validation pipelines that ensure all AI systems are rigorously validated for compliance, performance, and patient safetyCollaborating with AI product teams, clinical and technical stakeholders, DevOps engineers, and the AI Governance Committee to engineer scalable data flows that support model validation, real-time monitoringBuilding and maintaining robust ETL pipelines for structured and unstructured clinical data from EHR, imaging, and text sourcesDesigning systems to automate data preparation, reputed company tracking, and reproducibility for AI model inputs and outputsDeveloping data infrastructure for benchmarking and stress-testing models in clinical simulation environmentsCollaborating with DevOps and cloud teams to ensure deployment pipelines meet compliance and performance standardsSetting up and monitoring model tracking infrastructure for evaluation metrics and reputed company detectionAssisting in the development of standards and procedures affecting data management, design and maintenanceDocumenting all standards and proceduresEngineering and maintaining pipelines that support pre-deployment model validation and post-deployment monitoringCollaborating with Data Scientists and Clinical Product Owners to validate data integrity, reproducibility, and fairness in AI workflowsEnsuring compliance with HIPAA, ethical guidelines, and institutional governance policies on sensitive health data useBuilding dashboards and tools that provide observability across the ML lifecycle: data, models, outcomesDesigning, building, and deploying LLM-powered applications including clinical chatbots, copilots, and decision-support tools for end-users across MSHSDeveloping retrieval-augmented generation (RAG) pipelines that integrate reputed company databases with clinical knowledge sources, EHR data, and institutional documentsBuilding agentic platforms and multi-agent workflows using frameworks such as reputed company, reputed company, LangGraph, reputed company, or equivalentOperationalizing LLM deployment, including inference optimization, latency and cost tuning, model serving, and integration of safety guardrailsImplementing prompt engineering, prompt versioning, and structured prompt-evaluation workflows across model providers and versionsFine-tuning and adapting foundation models to clinical and operational use cases where appropriateBuilding LLM evaluation harnesses covering accuracy, hallucination, safety, bias, sycophancy, and clinical appropriateness, with red-teaming and stress-testing of deployed systemsEffectively communicating technical findings related to model and data integrity to governance teams, clinical stakeholders, and leadershipMaintaining clear and well-organized documentation of data workflows, platform architecture, and validation processesHelping write internal reports on data infrastructure resilience, validation system status, and operational riskStaying informed on industry best practices in data engineering and healthcare-focused machine learningPossessing an extremely flexible attitude and willingness to work with multiple types of technologies and languagesContinuous interest in updating skill sets and knowledge of trends in the Big Data Technology spaceWorking closely with cross-functional teams including data scientists, healthcare providers, and IT professionals to understand data requirements, develop solutions, and support data-driven decision-making Skills Bachelor's degree in Computer Science, Statistics, Mathematics, or related fieldKnowledge of at least one programming language among Scala, Python, Java, C, or C++Knowledge of big data technologies (e.g., Hadoop, Spark)Knowledge of Software Development LifecycleSelf-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing complex techniques or programs in a dynamic environmentAct as the major contributor in the development and operationalization of four different applications