DevOps Engineer

Job Description

This position is W2 only and is onsite three days a week.

Job Title : DevOps / Infrastructure

The Position

The CDI&A team collaborates with a broad range of stakeholders involved in the clinical development process (e.g., Clinical Science, Clinical Operations, Medical Writing, Quality, Regulatory).

It develops industry-leading solutions for highly complex business problems. To manage the design and pilot of these software products, they apply a high degree of ingenuity and creativity while maintaining a finger on the pulses of the rapidly changing tech and healthcare landscapes.

The Software Engineer will primarily be responsible for designing, developing, and deploying software which interacts with cutting-edge generative AI models and applications in collaboration with AI scientists, full stack developers, and others.

Their work will directly impact our ability to create and deliver innovative solutions that leverage AI to solve complex problems

and enhance user experiences.

The Opportunity :

Innovate and develop software applications to support clinical development

Identify and integrate AI / LLM capabilities to enhance data processing and

natural workflows.

Deployment and Monitoring : Develop and maintain robust deployment pipelines

for AI-enhanced applications. Monitor pipeline performance in production and

implement necessary improvements.

Code Quality and Documentation : Write clean, maintainable, and well-

documented code. Participate in code reviews and contribute to best practices in

software development.

Research and Innovation : Stay up-to-date with the latest advancements in

generative AI and machine learning. Evaluate new technologies and

methodologies to continuously improve our solutions.

Collaborate with Cross-Functional Teams : Work closely with data scientists,

engineers, and product managers to integrate generative AI capabilities into our

products and services.

Who You Are :

An experienced full stack developer capable of bringing your expertise to our

existing and upcoming AI applications / projects as both a leader and individual

contributor.

Someone with a clear understanding of the current landscape of AI & AI-based

applications, including potential benefits, limitations, and standard of practice.

Someone with experience in the automation and speed up of the software

development lifecycle.

Someone who brings additional skills to the software development process like

test automation, deployment maintenance, or ML model deployment / monitoring.

Minimum Requirements :

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.

Strong proficiency in either a front-end framework (Vue.js, React, or similar) and a backend web frameworks in Python and / or JavaScript (Django, FastAPI, Flask, Next.js, or similar)

2+ years experience with front-end frameworks (preferably Vue.js)

2+ years of developing and deploying AI / ML solutions or applications

Experience designing and developing RESTful APIs (with e.g. Python FastAPI).

Proficiency with containerized workflows and architectures (Podman, Docker,

Kubernetes)

3+ years of DevOps experience with cloud platforms, specifically AWS (EKS, RDS, EC2).

Experience with caching and data store technologies like Redis.

Experience with CI / CD pipelines and automation tools (GitLab CI, GitHub

Actions).

Proficiency in Infrastructure as Code tools (Terraform, CloudFormation) and

GitOps (Argo CD).

Experience with monitoring and observability tools (Portkey, Prometheus,

Grafana, ELK stack).

Experience with cloud data warehousing / analytics platforms (e.g., Snowflake,

BigQuery, Redshift).

Knowledge of system administration, particularly with Linux.

Experience with security best practices and compliance frameworks.

Strong incident management and root cause analysis skills.

Familiar with Agile methodologies

Excellent analytical and problem-solving skills with a track record of tackling

complex technical challenges.

Strong interpersonal and communication skills, with the ability to articulate

complex technical concepts to non-technical stakeholders.

Extensive experience with cloud platforms (e.g. AWS) and modern data

platforms (e.g., Snowflake).

Experience implementing chatbots, retrieval-augmented generation (RAG)

systems, and integrating LLMs into applications (AI-assisted automation)