ML Ops Generative AI Engineer

Educology Solutions; Washington (District of Columbia); Hybrid; USD 170,000 - 210,000; Full time

Job summary

Educology Solutions is seeking a Senior MLOps & Generative AI Engineer to drive enterprise-scale AI initiatives in a healthcare-oriented setting. You will design scalable ML infrastructure, CI/CD for AI workloads, and robust governance for models used across clinical and operational domains.

You will collaborate with AI Scientists, Data Engineers, and Software Engineers to deploy GenAI and ML solutions, emphasizing reliability, security, and regulatory compliance in production healthcare

Qualifications

• 5+ years of experience building and deploying production software, ML systems, or AI platforms.
• 1+ years of hands-on experience building production Generative AI or LLM-based applications.
• Strong Python programming skills and software engineering best practices.
• Experience with PyTorch, Hugging Face Transformers, TensorFlow or equivalent.
• Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
• Experience deploying AI/ML systems in 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.
• Experience implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.
• Experience building monitoring, observability, and alerting solutions for ML and AI systems.
• Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations.
• Experience designing secure, scalable, production-ready AI platforms and services.
• Strong communication and collaboration skills with the ability to work across technical and business teams.

Responsibilities

• Design, build, and maintain scalable ML infrastructure and pipelines for training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for ML workloads across dev, staging, and production.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, deployment automation.
• Implement scalable orchestration for batch and real-time ML inference workloads.
• Create robust monitoring to measure model performance, detect drift, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve MLOps efficiency and reliability.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation to production.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, and IaC.
• Identify gaps and architect scalable improvements to the ML platform ecosystem.
• Support enterprise AI governance, compliance, auditability, and model risk management.

Skills

Python programming

AI/ML lifecycle management

Distributed systems

Communication skills

Mentoring engineers

Tools

PyTorch

Hugging Face Transformers

TensorFlow

Pinecone

Weaviate

Chroma

FAISS

Milvus

Kubernetes

AWS

Azure

GCP

Job description

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.

Preferred

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.

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 foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Required Qualifications

• 5+ years of experience building and deploying production software, ML systems, or AI platforms.
• 1+ years of hands-on experience building production Generative AI or LLM-based applications.
• Strong programming skills in Python and experience with software engineering best practices.
• Experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.
• Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
• Experience deploying AI/ML systems in 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.
• Experience implementing CI/CD pipelines, infrastructure automation, and MLOps best practices.
• Experience building monitoring, observability, and alerting solutions for ML and AI systems.
• Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations.
• Experience designing secure, scalable, production-ready AI platforms and services.
• Strong communication and collaboration skills with the ability to work across technical and business teams.

Preferred Qualifications

• Previous experience implementing Generative AI and MLOps solutions within healthcare environments.
• Experience working with EPIC or healthcare interoperability platforms.
• Understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices.
• Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling.
• Experience with GPU infrastructure optimization and scalable inference architectures.
• Familiarity with multi-agent AI systems and autonomous workflows.
• Experience with event-driven architectures, streaming pipelines, and real-time inference systems.
• Exposure to model fine-tuning techniques including LoRA, PEFT, RLHF, or domain adaptation strategies.
• Experience with enterprise AI platform architecture and internal developer platforms.
• Prior experience mentoring engineers and leading technical initiatives.

Education

• or

7+ years of relevant experience without a degree (Required)

Experience in lieu of Bachelor's Degree.

Certification/Licensure

No specific certification or licensure requirements

Experience

5 to 7 years of relevant experience