ML Ops Generative AI Engineer

We are seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join our growing AI organization and help advance current and future initiatives applying machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence.

This role combines two critical focus areas (If a candidate is primarily focused in one of the two, preferred area is Gen AI Engineer) - Leadership is willing to strengthen/train in MLOps if strong enough in AI:

MLOps Engineering — building and scaling enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation capabilities.

Generative AI Engineering — designing, architecting, deploying, and optimizing secure, production-ready GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.

Strong Python coding experience and ability to demonstrate in live coding exercise

As a Senior Engineer, you will partner closely with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at enterprise scale. You will play a key role in shaping the organization’s AI platform strategy, driving best practices, and delivering scalable, secure, and reliable AI systems in production healthcare environments.

KEY RESPONSIBILITIES

MLOps Engineering Responsibilities

Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.

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

Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.

Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.

Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.

Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.

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

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

Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.

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

Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.

Generative AI Engineering Responsibilities

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

Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.

Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.

Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.

Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.

Build AI evaluation and benchmarking frameworks to measure 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 for enterprise healthcare environments.

Design scalable GenAI APIs and microservices supporting 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.

Research and evaluate emerging GenAI technologies, open-source frameworks, and foun…