MKS Vision - Senior Artificial Intelligence Engineer

About the Role :

As PennEngineering accelerates its Speed of Now transformation (respond in 1 hour, quote in 1 day, samples in 1 week, finished product in 1 month), we are building an internal capability to design, develop, and deploy AI-powered workflows, automation, and agentic solutions that improve speed, consistency, and quality across the business.

The AI Engineer is responsible for converting validated business user stories into production-ready AI-powered workflows and AI agents. Some use cases will be addressed through AI-driven workflow automation, while others will require multi-step agentic AI.

This role operates as part of a cross-functional delivery model, supported by solution architecture, IS/IT engineering, DevOps, as well as engagement with business stakeholders and subject-matter experts.

Role & Responsibilities :

- 3- 6 years of overall software engineering experience, with at least 2 years focused on AI/LLM application development and agentic systems

- Production experience building and operating multi-step AI agents using LangChain, LangGraph, CrewAI, AutoGen, AWS Bedrock Agents, or equivalent

- Strong Python engineering skills well-structured, testable, production-quality code

- Hands-on experience with RAG architectures: chunking strategies, embedding models, vector store selection, retrieval optimization, and re-ranking

- Practical knowledge of prompt engineering at scale: structured prompts, chain-of-thought, few-shot design, and prompt evaluation

- Experience with AWS cloud services in a production context (Lambda, Bedrock, ECS, S3, API Gateway, CloudWatch)

- Working knowledge of CI/CD practices and deploying AI solutions through automated pipelines

- Understanding of AI observability: logging agent reasoning traces, tracking token usage, monitoring for quality drift

- Proficient in the use of AI-assisted coding tools (Cursor, Claude Code, Amazon Kiro, or similar) as a core part of your engineering workflow with the ability to define, follow, and improve structured coding workflows that leverage these tools effectively

- Strong communication skills able to translate technical designs into clear explanations for non-technical stakeholders

- Bachelors degree in computer science, Engineering, or a related technical field

Preferred Qualifications :

- Experience with structured agent evaluation frameworks and LLM testing methodologies (e.g., LangSmith, RAGAS, or custom harnesses)

- Familiarity with Model Context Protocol (MCP) and emerging standards for tool-use and agent interoperability

- Experience with fine-tuning or adapting open-source models (Llama, Mistral, or similar) for domain-specific tasks

- Knowledge of data engineering, ETL pipelines, and working with enterprise data platforms (Snowflake, Databricks, or similar)

- Experience in manufacturing, supply chain, or industrial environments

- Familiarity with agile methodologies and product development practices

- Experience with multimodal AI applications document understanding, image analysis, or structured data extraction