Senior MLOps & GenAI Engineer

Salary: $84000 - 140000.8 per year Requirements: We require 5+ years of experience building and deploying production software, machine learning systems, or AI platforms. We require at least 1 year of hands-on experience developing production Generative AI or LLM-based applications. We need strong programming expertise in Python, along with solid software engineering practices. We expect experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or similar tools. We require practical experience with RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks. We need experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or comparable technologies. We require experience deploying AI/ML solutions in cloud environments such as AWS, Azure, or GCP. We expect a strong understanding of APIs, distributed systems, microservices, and scalable backend design. We need experience with Kubernetes, containers, orchestration, and cloud-native infrastructure. We require experience building CI/CD pipelines, infrastructure automation, and MLOps practices. We expect experience creating monitoring, observability, and alerting solutions for ML and AI systems. We need a strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations. We require experience designing secure, scalable, production-ready AI platforms and services. We value strong communication and collaboration skills with the ability to work across technical and business partners. We prefer experience applying Generative AI and MLOps solutions in healthcare settings. We prefer familiarity with EPIC or healthcare interoperability platforms. We prefer understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices. We prefer experience with AI governance frameworks, LLM evaluation methods, and AI safety tooling. We prefer experience with GPU infrastructure optimization and scalable inference architectures. We prefer exposure to multi-agent AI systems and autonomous workflows. We prefer experience with event-driven architectures, streaming pipelines, and real-time inference systems. We prefer familiarity with fine-tuning methods such as LoRA, PEFT, RLHF, or domain adaptation. We prefer experience with enterprise AI platform architecture and internal developer platforms. We prefer prior experience mentoring engineers and leading technical initiatives. We require either 5+ years of relevant experience with a degree or 7+ years of relevant experience without a degree. We do not require any specific certification or licensure. Responsibilities: We design, build, and maintain scalable ML infrastructure and pipelines for training, deployment, monitoring, governance, and lifecycle management. We develop and refine CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments. We create reusable platform capabilities such as feature stores, model registries, experimentation frameworks, artifact management, and deployment automation. We implement orchestration and workflow solutions for both batch and real-time ML inference. We establish monitoring systems to track model performance, detect drift, assess data quality, and support production reliability. We build automation tools and self-service features that improve the speed, scale, and dependability of MLOps operations. We work closely with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation to enterprise deployment. We apply software engineering standards to AI/ML systems, including testing, observability, resiliency, security, version control, and infrastructure as code. We identify gaps in our ML platform ecosystem and design scalable solutions to address them. We support enterprise AI governance, compliance, auditability, and model risk management needs. We ensure platform scalability, reliability, security, and operational excellence across AI/ML environments. We lead the architecture, design, and rollout of enterprise Generative AI solutions using LLMs, foundation models, and agentic AI systems. We design and implement Retrieval-Augmented Generation pipelines with vector databases, embeddings, semantic search, reranking, and retrieval optimization. We build LLM orchestration frameworks using tools such as LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks. We develop advanced prompt engineering, prompt chaining, context handling, and agent workflows to improve accuracy and reliability. We evaluate and apply fine-tuning, parameter-efficient tuning, and prompt-based optimization for domain-specific use cases. We create AI evaluation and benchmarking frameworks to measure hallucinations, response quality, grounding accuracy, toxicity, bias, latency, and business outcomes. We implement safety guardrails, governance controls, content fi