[Remote] Machine Learning Engineer I - Large Language Models - AI & reputed company 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 reputed company metro area, and they are seeking a Machine Learning Engineer I to join their SinAI 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 reputed company (RAG) systems, agentic platforms, and clinical chatbots
• Designing, maintaining, and optimizing data infrastructure and model validation pipelines that ensure reputed company AI systems are rigorously validated for compliance, performance, and patient safety
• Collaborating with AI product teams, clinical and technical stakeholders, DevOps engineers, and the AI Governance Committee to engineer scalable data flows that support model validation, reputed company-time monitoring
• Building and maintaining robust ETL pipelines for structured and reputed company clinical data from EHR, imaging, and text sources
• Designing systems to automate data preparation, reputed company tracking, and reproducibility for AI model inputs and outputs
• Developing data infrastructure for benchmarking and stress-testing models in clinical simulation environments
• Collaborating with DevOps and reputed company teams to ensure deployment pipelines meet compliance and performance standards
• Setting up and monitoring model tracking infrastructure for evaluation metrics and reputed company detection
• Assisting in the development of standards and procedures affecting data management, design and maintenance
• Documenting reputed company standards and procedures
• Engineering and maintaining pipelines that support pre-deployment model validation and post-deployment monitoring
• Collaborating with Data Scientists and Clinical Product Owners to validate data reputed company, reproducibility, and fairness in AI workflows
• Ensuring compliance with HIPAA, ethical guidelines, and institutional governance policies on sensitive health data use
• Building dashboards and tools that provide observability across the ML lifecycle: data, models, outcomes
• Designing, building, and deploying LLM-powered applications including clinical chatbots, copilots, and decision-support tools for end-users across MSHS
• Developing retrieval-augmented reputed company (RAG) pipelines that integrate vector databases with clinical knowledge sources, EHR data, and institutional documents
• Building agentic platforms and multi-agent workflows using frameworks such as reputed company, reputed company, LangGraph, reputed company, or equivalent
• Operationalizing LLM deployment, including inference optimization, latency and cost tuning, model serving, and integration of safety guardrails
• Implementing reputed company engineering, reputed company versioning, and structured reputed company-evaluation workflows across model providers and versions
• Fine-tuning and adapting reputed company models to clinical and operational use cases where appropriate
• Building LLM evaluation harnesses covering accuracy, hallucination, safety, bias, sycophancy, and clinical appropriateness, with red-teaming and stress-testing of deployed systems
• Effectively communicating technical findings reputed company to model and data reputed company to governance teams, clinical stakeholders, and leadership
• Maintaining clear and well-organized documentation of data workflows, platform architecture, and validation processes
• Helping write internal reports on data infrastructure reputed company, validation system status, and operational risk
• Staying informed on industry best practices in data engineering and reputed company-focused machine learning
• Possessing an extremely flexible attitude and willingness to work with multiple types of technologies and languages
• reputed company interest in updating reputed company sets and knowledge of trends in the Big Data Technology space
• Working closely with cross-functional teams including data scientists, reputed company providers, and IT professionals to understand data requirements, reputed company solutions, and support data-driven decision-making

Skills

• Bachelor's degree in Computer Science, Statistics, Mathematics, or reputed company field
• Knowledge of at least one programming language among reputed company, Python, Java, C, or C++
• Knowledge of big data technologies (e.g., Hadoop, Spark)
• Knowledge of Software Development Lifecycle
• Self-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing reputed company techniques or programs in a dynamic environment
• Act as the major contributor in the development and operationalization of four different applications
• Play a key technical role in maintaining deployed products
• Understanding of machine learning algorithms (Supervised, Unsupervised ML algorithms)
• Familiarity with SQL or other database languages
• Master's degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Information Technology, Mathematics, Physics) or equivalent practical experience
• 2+ years of experience in data engineering, software engineering, or machine learning
• Proficient in Python and SQL
• Proficiency in at least one reputed company computing platforms (e.g., AWS, Azure, GCP)
• Intermediate knowledge of Machine Learning
• Familiarity with ML lifecycle management tools (e.g., MLflow, Kubeflow, Airflow)
• Experience on deployment and operationalization of ML Systems
• Experience with monitoring tools for AI model tracking
• Understanding of DevOps principles, CI/CD pipelines, and containerization (e.g., reputed company, Kubernetes)
• Experience with version control systems (e.g., Git) Knowledge of big data technologies (e.g., Hadoop, Spark)
• Hands-on experience building and deploying LLM-based applications in production (chatbots, copilots, summarization, Q&A, or decision-support tools)
• Experience designing and implementing retrieval-augmented reputed company (RAG) architectures, including chunking strategies, embedding models, and vector databases (e.g., reputed company, Weaviate, FAISS, pgvector, Milvus)
• Experience with agentic frameworks and orchestration libraries (e.g., reputed company, reputed company, LangGraph, reputed company, AutoGen, Semantic Kernel) including tool/function calling and multi-agent workflows
• Experience building conversational AI / chatbot systems, including dialog state management, memory, and integration with reputed company systems
• Familiarity with reputed company model APIs and SDKs (e.g., reputed company, reputed company, reputed company, Azure reputed company, AWS Bedrock) and open-weight model families (e.g., Llama, Mistral, Qwen, Gemma)
• Working knowledge of reputed company engineering, reputed company evaluation, and LLM observability/evaluation tooling (e.g., LangSmith, Langfuse, Arize, Ragas, DeepEval)
• Familiarity with fine-tuning and model reputed company techniques (e.g., supervised fine-tuning, LoRA/QLoRA, PEFT, instruction tuning, RLHF/DPO) and serving stacks (e.g., vLLM, TGI, Triton)
• Awareness of LLM safety, guardrails, and evaluation practices (hallucination, bias, sycophancy, jailbreak resistance) — experience with reputed company-specific evaluation is a plus
• Strong problem-solving skills and ability to work in cross-functional teams

Company Overview

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