AI Orchestration Engineer

Jobtailor; Massachusetts; On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking a data/AI engineer to design and build enterprise AI orchestration frameworks that coordinate multiple models, agents, tools, APIs, and enterprise applications. You will develop scalable data pipelines, implement MLOps/LLMOps, and establish responsible AI governance and observability across AI-enabled business capabilities.

Work will involve building reusable patterns for rapid deployment, ensuring data accessibility for AI use cases, and delivering governance, risk

Qualifications

• Bachelor's degree in CS/AI or equivalent.
• 3–5 years in Data Engineering, AI Engineering, ML/Software Engineering, or related fields.
• Experience building large-scale data pipelines and distributed data-processing solutions.
• Proficiency in Python, SQL, APIs, workflow automation, and cloud-native architectures.
• Strong communication and stakeholder collaboration.
• Experience with LangGraph, LangChain, Databricks, or similar frameworks.

Responsibilities

• Design and engineer AI orchestration frameworks coordinating models, agents, tools, APIs, and enterprise applications.
• Develop agent-to-agent and human-in-the-loop workflows for complex operational and analytical processes.
• Build reusable orchestration patterns for rapid deployment of AI-enabled business capabilities.
• Design and develop scalable data pipelines supporting AI, analytics, and agentic workflows.
• Build enterprise data products optimized for AI consumption.
• Design and implement enterprise RAG architectures.
• Develop reusable AI platform components supporting multiple use cases and business domains.
• Implement MLOps and LLMOps deployment, monitoring, versioning, and governance capabilities.
• Implement Responsible AI guardrails, governance controls, and model risk management processes.
• Build AI observability, evaluation, telemetry, and performance measurement solutions.
• Accelerate delivery of AI-enabled business capabilities through reusable orchestration frameworks.
• Increase adoption of governed enterprise AI services.
• Improve enterprise data accessibility for AI use cases.
• Enhance reliability, observability, and auditability of AI systems.
• Reduce operational complexity through agentic automation and intelligent workflows.

Skills

Strong communication

Stakeholder collaboration

Experience with data pipelines

Experience in AI/ML engineering

Education

Bachelor's degree in Computer Science, Data Engineering, Information Systems, AI, or equivalent practical experience

Tools

LangGraph

Semantic Kernel

CrewAI

AutoGen

LangChain

Databricks

Snowflake

Spark

Kafka

Airflow

Delta Lake

Iceberg

Job description

Responsibilities

• Design and engineer AI orchestration frameworks coordinating multiple models, agents, tools, APIs, and enterprise applications
• Develop agent-to-agent and human-in-the-loop workflows automating complex operational and analytical processes
• Build reusable orchestration patterns for rapid deployment of AI-enabled business capabilities
• Design and develop scalable data pipelines supporting AI, analytics, and agentic workflows
• Build enterprise data products optimized for AI consumption
• Design and implement enterprise RAG architectures
• Develop reusable AI platform components supporting multiple use cases and business domains
• Implement MLOps and LLMOps deployment, monitoring, versioning, and governance capabilities
• Implement Responsible AI guardrails, governance controls, and model risk management processes
• Build AI observability, evaluation, telemetry, and performance measurement solutions
• Accelerate delivery of AI-enabled business capabilities through reusable orchestration frameworks
• Increase adoption of governed enterprise AI services
• Improve enterprise data accessibility for AI use cases
• Enhance reliability, observability, and auditability of AI systems
• Reduce operational complexity through agentic automation and intelligent workflows

Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Artificial Intelligence, or equivalent practical experience
• 3–5 years of experience in Data Engineering, AI Engineering, Machine Learning Engineering, Software Engineering, or related disciplines
• Strong experience developing large-scale data pipelines and distributed data-processing solutions
• Experience with Python, SQL, APIs, workflow automation, and cloud-native architectures
• Strong communication and stakeholder collaboration skills
• Preferred: LangGraph, Semantic Kernel, CrewAI, AutoGen, LangChain, or similar frameworks
• Preferred: Databricks, Snowflake, Spark, Kafka, Delta Lake, Iceberg, and Airflow
• Experience with vector databases, semantic search, and enterprise RAG platforms
• Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks
• Knowledge of Responsible AI, data governance, and model risk management

Core Competencies

Demonstrates expertise in designing and engineering AI orchestration frameworks, developing scalable data pipelines, and implementing MLOps and LLMOps capabilities. Proficient in building enterprise data products and ensuring Responsible AI governance and model risk management.

Highest-signal resume keywords

• AI Orchestration Frameworks
• Data Pipeline Development
• MLOps Implementation
• Responsible AI Governance
• Cloud-Native Architectures

ATS Optimization Keywords

Hard Skills

• Python
• SQL
• Data Engineering
• Machine Learning Engineering
• Workflow Automation
• Distributed Data Processing
• AI Observability
• Model Risk Management
• Enterprise RAG Architectures
• Agentic Automation

Soft Skills

• Strong Communication
• Stakeholder Collaboration

Industry Keywords

• AI Engineering
• Data Governance
• Model Risk Management
• Enterprise Applications
• AI-Enabled Business Capabilities

Tools & Technologies

• LangGraph
• Semantic Kernel
• CrewAI
• AutoGen
• LangChain
• Databricks
• Snowflake
• Spark
• Kafka
• Airflow