AI Engineer I

RepliGen Corporation•Waltham, MA

1d•$130,000 - $160,000

About The Position

Repligen is seeking an AI Engineer to help drive enterprise-wide adoption of AI during a period of transformation and investment in capabilities, technologies, and data. The role is a contributing member of the Data Platform team, supporting deployment and management of the enterprise data and AI platform. The AI Engineer designs reusable components, starter kits, and developer-first patterns that help teams build AI solutions consistently. The role embeds governance into code, turns best practices into repeatable approaches, and reduces time to value for new use cases. It also develops and operates AI products and services, with an emphasis on engineering excellence, reliability, scalability, and practical impact.

Requirements

Bachelor’s degree in computer science, Statistics, Data Science, Information Systems, or a related field.
At least 3 years of experience with AI technologies, particularly GenAI, LLM, Agentic AI, or MLOps, delivering enterprise-scale, production-grade solutions.
Hands-on experience building agentic AI systems, including multi-agent workflows, orchestration, tool integration, and failure handling, using frameworks such as AutoGen or CrewAI.
Experience deploying and scaling AI solutions on platforms including Azure AI Foundry, Copilot Studio, or Google Vertex AI.
Hands-on experience with 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.
Experience with advanced prompt engineering and optimization techniques.
Experience designing and implementing RAG architectures.
Experience with embeddings, semantic search, and contextual retrieval strategies.
Experience with vector databases such as Pinecone, FAISS, or Weaviate, or AI data cloud platforms such as Databricks or Snowflake.
Strong background in API development, microservices architecture, and distributed systems for 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 AI/ML solutions using CI/CD and infrastructure as code (Terraform), and monitoring, debugging, and managing them in AWS, Azure, or GCP cloud environments.

Responsibilities

Build and orchestrate multi-agent AI systems for task planning, data retrieval, and execution across business workflows.
Develop and refine prompt engineering approaches, including prompt chaining, evaluation, and guardrails.
Build and maintain retrieval-augmented generation (RAG) pipelines, including data ingestion and indexing, embedding generation, retrieval optimization, and response grounding and validation.
Develop scalable backend services using Python frameworks for data processing.
Design and manage embedding pipelines and vector search infrastructure.
Integrate LLM capabilities from platforms such as OpenAI, Anthropic, and Microsoft into enterprise applications.
Build and maintain scalable APIs and microservices that expose AI capabilities across systems and applications.
Collaborate with data engineering and platform teams to support reliable data pipelines and access to high-quality data sources.
Deploy, monitor, and optimize AI solutions in cloud environments, with attention to 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 to support 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.
Develop reusable platform components and repeatable developer patterns that incorporate governance and best practices.