Generative AI Cloud Operations Engineer - Evinova

Operating at the intersection of healthcare, technology, data, and analytics, we are helping unlock the full potential of digital health, transforming how clinical research is conducted, how care is delivered, and how patients experience healthcare. Our solutions are built to scale, driving efficiency, improving decision-making, and ultimately delivering better outcomes for patients worldwide.

At Evinova, we are driven by a shared purpose to transform health through data and digital innovation. Our teams collaborate across disciplines to solve complex challenges, continuously learning and evolving in a fast-paced, high-impact environment.

We also recognize the importance of flexibility and balance. Our ways of working support both individual needs and team collaboration. To foster connection and collaboration, employees are expected to work from the office three days per week, creating opportunities for in-person teamwork, innovation, and meaningful connection.

Introduction to Role:

The Machine Learning and Artificial Intelligence Operations team (ML/AI Ops) is a newly formed platform team that will spearhead the design, creation, and operational excellence of our LLM-based agent deployments, multi-agent orchestration, and conversational AI systems pipelines to catalyze and accelerate science led innovations.

This team is responsible and accountable for the design, implementation, deployment, health and performance of all LLM-based applications. We manage ML/AI and broader cloud resources, automating operations through infrastructure-as-code and CI/CD pipelines, and ensure best-in-class operations – striving to push even beyond mere compliance with industry standards such as Good Clinical Practices (GCP) and Good Machine Learning Practice (GMLP).

As a Generative AI Cloud Operations Engineer for clinical trial design, planning, and operational optimization on our team, you will lead the development and management of AI operations systems for our trial management and optimization SaaS product. You will collaborate closely with our AI Engineers to transition projects from embryonic research into production-grade AI capabilities, utilizing advanced tools and frameworks to optimize model deployment, governance, and infrastructure performance.

This position requires a deep understanding of cloud-native agentic Generative AI deployment methodologies and technologies, AWS infrastructure, and the unique demands of regulated industries, making it a cornerstone of our success in delivering impactful solutions to the pharmaceutical industry.

Accountabilities:

Operational Excellence

• Drive the creation of proactive capability and process enhancements that ensures enduring value creation and analytic compounding interest.

• Design and implement resilient cloud Genereative AI agent operational capabilities to maximize our system A-bilities (Learnability, Flexibility, Extendibility, Interoperability, Scalability).

• Drive precision and systemic cost efficiency, optimized system performance, and risk mitigation with a data-driven strategy, comprehensive analytics, and predictive capabilities at the tree-and-forest level of our Generative AI-based systems, workloads and processes.

ML/AI Cloud Operations and Engineering

• Develop and manage GenAI Ops systems for clinical trial design, planning and operational optimization.

• Integrate LLM proxies/routers including LiteLLM Proxy/Router or other solutions

• Ensure proper RAG pipeline optimization and scaling

• Integration of token usage, latency, response quality, and hallucination detection tools at a platform level.

• Partner closely with AI Engineers and data scientists to shepherd projects from embryonic research stages into production-grade agentic Generative AI capabilities.

• Leverage and teach modern tools, libraries, frameworks and best practices to design, validate, deploy and monitor Generative AI agents in production (including LangChain, LangGraph, Google ADK, Langfuse, DSPy, Arize Phoenix, Pinecone, Weaviate, Splunk, Grafana, Prometheus, Xray, and more)

• Enhance system scalability, reliability, and performance through effective infrastructure and process management.

• Ensure that any prediction we make is backed by deep exploratory data analysis and evidence, interpretable, explainable, safe, and actionable.

• Leverage Vertex AI, Azure Foundry, OpenAI, Anthropic, and other foundation model platforms to provide reliable and stable access to LLMs

Personal Attributes:

• Customer-obsessed and passionate about building products that solve real-world problems.

• Highly organized and detail-oriented, with the ability to manage multiple initiatives and deadlines.

• Collaborative and inclusive, fostering a positive team culture where creativity and innovation thrive.

• Know when to ask for help and when to help others proactively.

Essential Skills/Experience:

• High school diploma or GED required.

• Minimum of 2 years of hands-on experience deploying, operating, and maintaining Generative AI agents, workflows, or applications in production environments.

• Strong understanding of the challenges associated with production GenAI systems, including reliability, scalability, latency, cost optimization, observability, evaluation, and model performance.

• Hands-on experience deploying agentic AI solutions using frameworks such as LangChain, LangGraph, LlamaIndex, Google ADK, Strands Agents, or similar.

• Strong experience with LLM evaluation and observability, using platforms such as Arize Phoenix, Langfuse, Braintrust, Freeplay, or comparable tools.

• Strong software engineering skills in Python and/or TypeScript, with experience building production-quality systems.

• Deep expertise with AWS cloud services, including deploying and operating cloud-native AI/ML workloads.

• Strong experience with infrastructure as code, including AWS CDK using Python and/or TypeScript.

• Experience with containerization and orchestration technologies, including Docker and Kubernetes.

• Strong understanding of the data science and machine learning lifecycle, with demonstrated experience moving models and AI capabilities from experimentation through production deployment and ongoing operations.

• Experience operationalizing RAG pipelines, LLM applications, or multi-agent systems in production is strongly preferred.

• Demonstrated ability to stay current with rapidly evolving Generative AI models, frameworks, tooling, evaluation techniques, and engineering practices.

• Proven ability to partner effectively with AI/ML engineers, data scientists, software engineers, product teams, and other cross-functional stakeholders.

• Strong written and verbal communication skills, with the ability to clearly document technical solutions, operational processes, and system performance.

SO, WHAT’S NEXT?

To be considered for this exciting opportunity, please complete the full application on our website at your earliest convenience – it is the only way that our Recruiter and Hiring Manager can know that you feel well qualified for this opportunity. If you know someone who would be a great fit, please share this posting with them.

Where can I find out more?

• Explore what we’re building:

• Stay connected and see our impact in action:

Apply today to bring smarter, faster clinical trials to life!

Evinova is an equal opportunity employer that is committed to diversity and inclusion and providing a workplace that is free from discrimination. Evinova is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing [email protected].

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