Senior AI Application Engineer

Company Description

At EVERSANA, we are proud to be certified as a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 7,000 employees is committed to creating and delivering next-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 650 clients ranging from innovative biotech start-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us!

Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs. We are deliberate and self-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve. We are EVERSANA.

Job Description

THE POSITION:

As a Mid-Level AI / Agent Application Engineer, you will be the core builder of the organization’s new agentic workforce. Working under the guidance of the Chief AI & Analytics Officer, you will develop the actual agents, write the APIs (tools) the agents will use, and optimize the data pipelines that feed context to the AI.

ESSENTIAL DUTIES AND RESPONSIBILITIES:
Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:

• Architect, design and implement scalable, multi-agent systems that automate complex, multi-step business processes.
• Translate existing processes, and develop novel multi-agent architectures for new opportunities
• Implement agentic solutions leveraging agent orchestration frameworks (e.g., Google ADK, LangGraph, CrewAI, etc.).
• Design secure “tool-calling” architectures, allowing LLMs to interact with internal databases, CRMs, and APIs safely.
• Implement LLMOps/AgentOps best practices
• Mitigate AI-specific security risks, such as prompt injection, hallucination loops, and unauthorized tool execution.
• Demonstrate a commitment to diversity, equity, and inclusion through continuous development, modeling inclusive behaviors, and proactively managing bias.
• All other duties as assigned.

EXPECTATIONS OF THE JOB:

• Travel: Some travel may be required for meeting with clients, stakeholders, or off-site personnel/management
• Hours: 40 hours per week, Monday to Friday

The above list reflects the general details necessary to describe the expectations of the position and shall not be construed as the only expectations that may be assigned for the position.

An individual in this position must be able to successfully perform the expectations listed above.

Qualifications

MINIMUM KNOWLEDGE, SKILLS AND ABILITIES:

The requirements listed below are representative of the experience, education, knowledge, skill and/or abilities required.

• Ph.D. in Computer Science, Engineering (Electrical, Mechanical, Chemical), Mathematics, Physics, Artificial Intelligence, Software Engineering, or a closely related field.
• 7+ years of software engineering experience, with 3+ years specifically in generative AI, LLMs, or cognitive architectures.
• Expert-level proficiency in Python and/or TypeScript.
• Experience with MCP architectures
• Proficiency in Rust
• Deep understanding of agentic design patterns (e.g., ReAct, Plan-and-Solve, Reflection, Tree of Thoughts, etc.).
• Extensive experience with LLM APIs (OpenAI, Anthropic, Google Gemini) and open-weights models (Llama 3, Mistral, etc.).
• Experience with vector and graph databases
• Experience with RAG (Retrieval-Augmented Generation) architectures (e.g., GraphRAG, hybrid search).
• Strong background in cloud architecture (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).

PREFERRED QUALIFICATIONS:

• Experience in enterprise scale deployment of multi-agent architectures.
• Expert-level proficiency in Rust.
• Extensive experience with RA…