AI Engineer I

Role Overview

We are seeking a unique individual with a passion for AI technology who can help to drive forward enterprise-wide scale AI adoption. Repligen is in a transformational period where we are accelerating our capabilities, technologies, and data to augment our scientific mission.

As an AI Engineer, you will unlock and accelerate AI by designing the reusable components, starter kits, and opinionated developer first patterns for the Platform. This person will be a key contributing member of the Data Platform, driving forward the deployment and management of Repligen's enterprise platform for data & AI. This is a critical role for a highly skilled AI engineer who is passionate about building innovative solutions. This role operates at the platform level, shaping how teams build AI on the platform by turning best practices into predictable and repeatable patterns embedding governance into code, and reducing time to value for new use cases. You will be at the heart of our AI initiatives, ensuring that we maintain engineering excellence while delivering impactful products.

Key Responsibilities

• Build and orchestrate multi-agent AI systems using state-of-the-art platforms, enabling agents to collaborate on task planning, data retrieval, and execution across complex business workflows.
• Implement and continuously refine prompt engineering strategies, including prompt chaining, evaluation, and guardrails to improve accuracy and reliability
• Build and maintain RAG (Retrieval-Augmented Generation) pipelines, including Data ingestion and indexing, Embedding generation, Retrieval optimization & Response grounding and validation
• Development of scalable backend services using Python frameworks for data processing
• Design and manage embedding pipelines and vector search infrastructure using tools such as Pinecone, FAISS, Weaviate, Databricks or Snowflake
• Integrate LLM capabilities from platforms like OpenAI, Anthropic, and Microsoft into enterprise applications
• Build and maintain scalable APIs and microservices to expose AI capabilities across systems and applications
• Collaborate with data engineering and platform teams to ensure reliable data pipelines and access to high-quality data sources
• Deploy, monitor, and optimize AI solutions in cloud environments (Azure, AWS, GCP), ensuring performance, scalability, and cost efficiency
• Implement evaluation frameworks and monitoring for LLM outputs, including quality metrics, drift detection, and feedback loops
• Troubleshoot and resolve issues in distributed AI systems, ensuring high availability and reliability
• Partner with business stakeholders to translate requirements into AI-driven solutions and automation opportunities
• Document architecture, design decisions, and best practices to support scalability and team adoption

Required Qualifications

• Bachelor's degree in computer science, Statistics, Data Science, Information Systems or related field
• 3+ years of experience with AI technologies, particularly GenAI, LLM, Agentic AI, or MLOps, and delivering enterprise-scale, production-grade solutions
• Hands-on experience in building agentic AI systems, including multi-agent workflows, orchestration, and tool integration using frameworks such as AutoGen or CrewAI and failure handling
• Experience deploying and scaling AI solutions on platforms including Azure AI Foundry, Copilot Studio, or Google Vertex AI
• Hands-on experience in machine learning and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Expertise in Generative AI and LLM application development using OpenAI and Anthropic APIs, including: Advanced prompt engineering and optimization techniques; Design and implementation of Retrieval-Augmented Generation (RAG) architectures; Embeddings, semantic search, and contextual retrieval strategies; Experience with vector databases such as Pinecone, FAISS, Weaviate or AI data cloud platform such as Databricks or Snowflake
• Strong background in API development, microservices architecture, and distributed systems, enabling scalable AI solution deployment
• Experience with knowledge graphs, graph databases, or hybrid retrieval systems to enhance contextual reasoning and data relationships
• Proficiency in Python programming
• Experience deploying using CI/CD, infrastructure as code (Terraform), monitoring, debugging & managing AI/ML solutions in cloud environments AWS, Azure & GCP