Senior MLOps, Generative AI Engineer

This is a fully remote position, open to applicants in United States, +25 more states.

📋 Description

• Design, develop, and maintain scalable machine learning infrastructure and pipelines that facilitate model training, deployment, monitoring, governance, and lifecycle management.

• Create and optimize CI/CD pipelines tailored for machine learning and AI workloads across development, staging, and production environments.

• Construct reusable capabilities for the ML platform, including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.

• Implement orchestration and workflow solutions that can scale for both batch and real-time ML inference workloads.

• Establish comprehensive monitoring systems to assess model performance, identify model drift, monitor data quality, and guarantee production reliability.

• Develop automation tools and self-service functionalities to enhance the efficiency, scalability, and reliability of MLOps processes.

• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation to enterprise production deployment.

• Apply best practices in software engineering to AI/ML systems, including testing, observability, resiliency, security, versioning, and infrastructure-as-code.

• Support enterprise AI governance, compliance, auditability, and model risk management requirements.

• Ensure the scalability, reliability, security, and operational excellence of AI/ML systems.

• Lead the architecture, design, and deployment of enterprise Generative AI solutions utilizing LLMs, foundation models, and agentic AI systems.

• Design and implement Retrieval-Augmented Generation (RAG) pipelines that leverage vector databases, embeddings, semantic search, reranking, and retrieval optimization techniques.

• Develop scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.

• Create advanced prompt engineering strategies, including prompt chaining, context management, and agent workflows to enhance LLM accuracy and reliability.

• Assess and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization techniques for domain-specific applications.

• Build AI evaluation and benchmarking frameworks to assess hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.

• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices in enterprise healthcare settings.

• Design scalable GenAI APIs and microservices that support high-throughput enterprise AI applications.

• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.

• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.

• Investigate and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to foster innovation and continuous improvement.

⛳️ Requirements

• 5+ years of experience in building and deploying production software, ML systems, or AI platforms.

• 1+ years of hands-on experience in developing production Generative AI or LLM-based applications.

• Proficient programming skills in Python and familiarity with software engineering best practices.

• Experience with major deep learning and LLM frameworks like PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.

• Practical experience in implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.

• Familiarity with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or similar technologies.

• Experience in deploying AI/ML systems within cloud environments including AWS, Azure, or GCP.

• Strong understanding of APIs, distributed systems, microservices, and scalable backend architectures.

• Experience with Kubernetes, containerization, orchestration, and cloud-native infrastructure.

• Knowledge of implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.

• Experience in developing monitoring, observability, and alerting solutions for ML and AI systems.

• Solid understanding of AI/ML lifecycle management, governance, model versioning, and production operations.

• Experience designing secure, scalable, production-ready AI platforms and services.

• Excellent communication and collaboration skills, with the ability to work effectively across technical and business teams.

🏝️ Benefits

• Medical, Dental, Vision plans

• Adoption, Fertility and Surrogacy Reimbursement up to $10,000

• Paid Time Off and Sick Leave

• Paid Parental & Family Caregiver Leave

• Emergency Backup Care

• Long-Term, Short-Term Disability, and Critical Illness plans

• Life Insurance

• 401k/403B with Employer Match

• Tuition Assistance – $5,250/year and discounted educational opportunities through Guild Education

• Student Debt Pay Down – $10,000

• Reimbursement for certifications and complimentary access to complete CEUs and professional development

• Pet Insurance

• Legal Resources Plan

• Colleagues have the opportunity to earn an annual discretionary bonus if established system and employee eligibility criteria is met.