Sr. AI Developer, Engineering

EVERSANA is hiring a Senior AI Developer, Engineering to design, build, and deploy enterprise AI solutions for pharmaceutical and healthcare marketing operations. The role leads client technical discovery, architects and develops AI workflows and enterprise integrations, and helps move solutions from prototypes into production.

Responsibilities:

• Lead technical discovery sessions with client business, IT, architecture, security, data, and operations teams
• Translate business objectives, workflows, and operational challenges into technical requirements, architecture designs, and implementation plans
• Assess enterprise application landscapes, integration requirements, data environments, security constraints, and operating models
• Serve as a trusted technical advisor to clients and internal stakeholders
• Communicate complex AI, data, platform, and integration concepts to both technical and executive audiences
• Identify technical risks, dependencies, assumptions, and mitigation strategies early in engagements
• Develop technical prototypes and proofs of concept to validate solution feasibility and accelerate decision-making
• Design end-to-end solutions that connect AI Agency capabilities with client systems, data sources, content repositories, workflows, and approval environments
• Build and configure production-quality integrations using APIs, webhooks, event-based patterns, secure file exchanges, connector frameworks, and enterprise integration platforms
• Develop client-specific configuration and deployment assets while preserving the integrity of the shared platform architecture
• Contribute hands-on code across backend services, integration components, data pipelines, AI workflows, and supporting user experiences
• Design and implement AI-enabled workflows using large language models, retrieval-augmented generation, agent orchestration, structured outputs, evaluation frameworks, and human approval checkpoints
• Implement authentication, authorization, identity federation, secrets management, data mapping, error handling, observability, and audit requirements
• Create technical prototypes when needed to validate feasibility, reduce ambiguity, or accelerate client decision-making
• Support the transition from prototype to stable, supportable production deployment
• Design, build, and maintain integrations using APIs, webhooks, event-driven architectures, secure file exchanges, and enterprise integration platforms
• Build and maintain data pipelines supporting reporting, analytics, and AI workflows
• Design data mapping, transformation, validation, and synchronization processes
• Partner with data engineering teams to support ETL/ELT and analytics initiatives
• Develop cloud-native solutions with a preference for Google Cloud Platform
• Implement CI/CD pipelines and automated deployment processes
• Build and maintain containerized applications using Docker and Kubernetes
• Ensure application reliability through monitoring, logging, alerting, and observability practices
• Maintain high standards of testing, code quality, scalability, and operational excellence
• Support production troubleshooting, performance optimization, and incident resolution
• Modeling inclusive behaviors and proactively managing bias
• All other duties as assigned*.*

Requirements:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
• 9+ years of experience in software engineering, solution engineering, technical consulting, platform implementation, or related engineering roles
• 5+ years designing and deploying production cloud-based AI, data, or enterprise application solutions
• Advanced programming skills in Python and at least one additional modern language such as JavaScript/TypeScript, C#, Java, or .NET
• Significant experience designing:
  • APIs
  • Data pipelines
  • Event-driven architectures
  • Enterprise integrations
  • Authentication and security frameworks
  • Distributed systems
• Hands-on experience with generative AI technologies including:
  • LLMs
  • Prompt engineering
  • RAG
  • Embeddings
  • Vector databases
  • Agent orchestration
  • AI evaluations
  • Observability and guardrails
• Experience with Git, CI/CD, automated testing, containers, and cloud-native development practices
• Strong communication, consulting, and stakeholder management skills with the ability to influence