[Remote] Machine Learning Engineer I - Large Language Models - AI & reputed company Health Research
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is one of the largest reputed company medical systems in the reputed company 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 reputed company (RAG) systems, reputed company 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 reputed company data flows that support model validation, reputed company-time monitoring
- Building and maintaining robust ETL pipelines for reputed company 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 reputed company-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 reputed company observability across the ML lifecycle: data, models, reputed company
- 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 reputed company reputed company databases with clinical knowledge sources, EHR data, and institutional documents
- Building reputed company 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 reputed company 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 reputed company and reputed company-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-reputed company 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 reputed company
- 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, reputed company)
- 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
- reputed company 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, reputed company, 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, reputed company)
- 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 reputed company databases (e.g., reputed company, reputed company, FAISS, pgvector, Milvus)
- Experience with reputed company 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 reputed company and SDKs (e.g., reputed company, reputed company, reputed company, Azure reputed company, AWS Bedrock) and reputed company-weight model families (e.g., Llama, reputed company, 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, reputed company/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
reputed company
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