Principal Orchestration Engineer

SUMMARY:

Motion Industries is seeking a Principal Orchestration Engineer to architect and build the next generation of intelligent applications. Reporting to the Director of Enterprise Intelligence you are the technical leader responsible for designing the Data-to-Intelligence pipelinethe systems frameworks and orchestration layers that transform our data into AI-powered agents and applications. This is NOT a machine learning research role. You wont be training models or fine-tuning LLMs. Instead youll be the expert at using AI as a building blockintegrating LLM APIs (OpenAI Anthropic etc.) with our data infrastructure to build production-grade intelligent systems. This is a hands-on technical leadership role. You will personally build production AI applications while establishing the architectural patterns frameworks and best practices that enable the broader team to scale our AI capabilities.

What Youll Actually Do

Building AI Applications (60-70% of your time):

Build AI Agents: Design and implement multi-agent systems using a combination of frameworks like LangGraph and embedded 3rd party agents like Genie and CoPilot that orchestrate LLM API calls to solve business problems

RAG Architecture: Build retrieval-augmented generation systems that ground LLM responses in Motions data (vector databases semantic search chunking strategies)

Data Integration: Connect AI agents to our data lakes operational systems and external APIs ensuring agents have access to the right context

Orchestration & Workflows: Design complex agent workflows (sequential parallel hierarchical) that coordinate multiple LLM calls and data operations

Evaluation & Monitoring: Build automated testing and evaluation frameworks to measure agent accuracy latency cost and business impact

Production Engineering: Debug and optimize production AI applications for performance cost and reliability

Technical Leadership (30-40% of your time):

Establish Standards: Define best practices for RAG architectures prompt engineering agent design patterns and LLMOps

Framework Development: Build internal frameworks and libraries that make it easy for other engineers to build AI applications

Mentorship: Code review pair programming and upskilling data engineers and software engineers on AI application development

Architecture: Design reference architectures for common AI use cases (conversational agents analytical assistants automation agents)

Collaboration: Partner with the Director of Enterprise Intelligence product teams business intelligence and business units to identify and prioritize high-value AI use cases

Thought Leadership: Document and share learnings internally; represent Motions AI capabilities Key Responsibilities AI Application Architecture &

Development (50%)

Design and build production AI agents and intelligent applications using LLM APIs (OpenAI Anthropic Azure OpenAI etc.)

Implement RAG (Retrieval-Augmented Generation) systems including vector database selection embedding strategies chunking logic and metadata tagging

Build multi-agent orchestration systems that coordinate LLM calls data retrieval and business logic

Create reusable frameworks and patterns for common AI application needs

Optimize for cost (token usage) latency and quality across all AI applications

Data-to-AI Pipeline Engineering (30%)

Design the semantic layer that helps AI agents understand Motions business logic data relationships and domain context

Build robust data pipelines that prepare chunk and embed data for AI consumption

Implement vector databases and semantic search systems

Ensure AI agents can reliably access and query our data warehouse and operational systems

Partner with data engineering team to optimize data flows for AI use cases

LLMOps & Production Excellence (15%)

Implement CI/CD pipelines for AI applications (versioning testing deployment)

Build comprehensive monitoring and observability for agent performance cost and quality

Establish Guardrails-as-Code to prevent data leakage prompt injection and ensure compliance

Create automated evaluation frameworks (evals) to measure agent accuracy and business outcomes

Partner with DevOps on infrastructure security and cost optimization Enablement & Leadership (5%)

Lead training workshops on AI application development for data and software engineers

Create documentation reference implementations and best practice guides

Mentor team members on prompt engineering agent design and RAG architectures

Collaborate with the Director of Enterprise Intelligence on team roadmap hiring strategy and capability development

Required Qualifications

Technical Expertise Software Engineering Foundation:

Expert-level Python (this is a Python-heavy role)

Strong SQL and data querying skills API design and integration experience (RESTful APIs webhooks event-driven systems)

Software engineering best practices (version control testing CI/CD code review)

Experience with modern application frameworks

Data Engineering Experience:

3 years working with modern data stacks (Databricks Azure)

Understanding of data modeling ETL/ELT patterns and data warehouse architecture

Experience with data orchestration tools

Knowledge of data quality governance and security practices

AI Application Development:

2 years building production applications using LLM APIs (OpenAI Anthropic Azure OpenAI etc.)

Hands-on experience with LLM frameworks (LangChain LlamaIndex LangGraph Haystack or similar)

Deep expertise building RAG (Retrieval-Augmented Generation) systems at scale

Production experience with vector databases (Pinecone Weaviate Milvus Chroma pgvector etc.)

Proven track record designing and implementing multi-agent systems or complex LLM workflows

Strong prompt engineering skills and understanding of LLM capabilities/limitations Infrastructure & DevOps:

Experience with containerization (Docker) and cloud platforms (AWS Azure GCP)

Understanding of API rate limiting caching and optimization strategies

Knowledge of security best practices for AI applications (data privacy prompt injection prevention)

Familiarity with monitoring and observability tools

Experience & Background

8 years in software engineering data engineering or related technical roles

2 years specifically building AI-powered applications (using LLMs via APIs)

3 years in a senior or lead capacity with demonstrated technical leadership

Proven ability to design and implement systems that scale to enterprise requirements

Track record of successfully leading technical initiatives from concept to production

Experience working in cross-functional teams and translating business needs into technical solutions

LICENSES & CERTIFICATIONS: None required.

SUPERVISORY RESPONSIBILITY: No Supervisory Responsibility

BUDGET RESPONSIBILITY: No

COMPANY INFORMATION:

Motion offers an excellent benefits package which includes options for healthcare coverage 401(k) tuition reimbursement vacation sick and holiday pay.

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