AI Engineer Pharma Sector

AI Engineer Pharma Sector

NEORIS now part of EPAM is a Digital Accelerator that helps companies step into the future. With more than 20 years of experience as Digital Partners to some of the world's leading organizations, we operate with over 4,000 professionals across 11 countries. We foster a multicultural, startup-oriented culture that promotes innovation, continuous learning, and the delivery of high-impact solutions for our clients.

We are seeking a talented and motivated AI Engineer to join our Connected Insights team. In this role, you will design, build, and deploy production-grade AI systems that generate business and scientific insights, particularly in competitive intelligence and strategy contexts.

You will work with both foundational and state-of-the-art AI methods, leveraging structured and unstructured data to create scalable, reliable, and high-impact AI-driven solutions. This includes developing LLM-powered applications, multi-agent systems, and advanced retrieval architectures. You will collaborate closely with cross-functional teams and internal stakeholders, translating strategic needs into robust AI products.

Key Responsibilities:

• Design and deliver production-grade AI systems, including LLM-powered applications for strategy teams.
• Architect and implement multi-agent systems and tool-calling frameworks supporting strategy workflows, data collection, and human-in-the-loop controls.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines and vector-based retrieval systems over external datasets.
• Collaborate within agile, cross-functional teams to align AI solutions with business objectives.
• Engage stakeholders to gather requirements and translate them into scalable AI architectures.
• Implement evaluation frameworks to assess model accuracy, coverage, consistency, interpretability, and bias.
• Monitor, maintain, and optimize AI models in production environments to ensure scalability, reliability, and performance.
• Document models, architectures, processes, and lessons learned, contributing to internal AI capability building.
• Ensure compliance with ethical standards, privacy regulations, and fairness guidelines in AI development and deployment.

Requirements:

• MSc, PhD, or equivalent practical experience in Artificial Intelligence, Machine Learning, or related fields.
• Strong proficiency in Python and solid software engineering best practices (testing, modularity, version control).
• Hands-on experience with Large Language Models (LLMs), embeddings, RAG pipelines, and vector databases.
• Experience designing and implementing multi-agent systems or tool-calling frameworks.
• Experience building evaluation frameworks for model accuracy, coverage, interpretability, decision consistency, and bias detection.
• Experience working with distributed systems and microservices architectures.
• Strong understanding of data structures, algorithms, and API design principles.
• Experience collaborating within multidisciplinary teams.
• Excellent analytical, problem-solving, and communication skills.

Desirable:

Machine Learning: • Experience developing and deploying agentic AI systems, including autonomous agents and multi-agent workflows. • Practical experience fine-tuning or customizing LLMs (e.g., GPT, Llama, or similar). • Knowledge of ML monitoring, observability, and performance tracking in production environments.

Software Engineering: • Contributions to open-source ML projects or libraries. • Experience with high-performance computing environments. • Knowledge of software design patterns and scalable architecture principles.

Cloud & Infrastructure: • Experience with ML deployment platforms such as KubeFlow or MLflow. • Familiarity with serverless architecture patterns. • Understanding of cloud cost optimization strategies for ML workloads. • Experience with infrastructure-as-code tools (Terraform, CloudFormation).

What do we offer?

• A career plan where you can choose your path: technical specialization or management
• Social benefits
• Continuous training
• Flexible working hours and work-life balance.