Lead AI Engineer - ML/LLM Ops & Agentic AI

shifttechnology; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Paid time off

Mental health support

Learning & development

Volunteer days (2/year)

Job summary

Shift Technology is seeking a hands-on engineering leader to mentor a small team and shape the roadmap for production AI systems and agentic applications. You will evolve a Databricks-based ML platform, design production workflows, and collaborate across product, data science, security, and reliability teams to deploy new capabilities.

The role emphasizes hands-on coding, backend services in Azure with Kubernetes and Terraform, and strong MLOps practices, with flexible remote/hybrid work and a

Qualifications

• Strong production experience with agentic AI systems and tool calling.
• Proficient in Python and object-oriented software design.

Responsibilities

• Design and deliver production agent workflows using orchestration, tool calling, retrieval-augmented generation, and structured outputs; establish when to use models versus deterministic code.
• Set operational standards for evaluation, prompt and agent versioning, tracing, cost controls, and release gates, including patterns that preserve tenant data isolation.
• Own the technical evolution of a Databricks-based ML platform, extending its model development, registry, deployment, and monitoring capabilities to support agents and prompts.
• Design the inference layer for internally hosted open-weight models and improve latency and throughput in partnership with cloud operations.
• Run focused prototypes of emerging AI approaches with measurable success criteria and a path to production.
• Coach and develop a team of two to five engineers, lead design and code reviews, and coordinate with product, security, architecture, subject matter, and reliability specialists.

Skills

Python

OO design

MLOps

Team leadership

English fluency

System architecture

Tools

Databricks

MLflow

Azure

Kubernetes

Terraform

Job description

Role overview

Lead a small engineering team while remaining hands-on in building and operating production AI systems. The role shapes the roadmap for agentic applications and LLM operations, evolves an established machine learning platform, and works across engineering, product, data science, cloud operations, security, and reliability teams to bring new capabilities into production.

Responsibilities

• Design and deliver production agent workflows using orchestration, tool calling, retrieval-augmented generation, and structured outputs; establish when to use models versus deterministic code.
• Set operational standards for evaluation, prompt and agent versioning, tracing, cost controls, and release gates, including patterns that preserve tenant data isolation.
• Own the technical evolution of a Databricks-based ML platform, extending its model development, registry, deployment, and monitoring capabilities to support agents and prompts.
• Design the inference layer for internally hosted open-weight models and improve latency and throughput in partnership with cloud operations.
• Run focused prototypes of emerging AI approaches with measurable success criteria and a path to production.
• Coach and develop a team of two to five engineers, lead design and code reviews, and coordinate with product, security, architecture, subject matter, and reliability specialists.

Requirements

• Demonstrated experience building production agentic AI systems, with strong knowledge of tool calling, MCP, retrieval-augmented generation, and evaluation.
• High proficiency in Python and experience designing maintainable software with object-oriented principles and sound engineering practices.
• Hands-on MLOps experience spanning experiment tracking, model registry, serving, and monitoring; Databricks and MLflow are relevant examples.
• Experience building backend services in Azure environments with Kubernetes, Terraform, and event-driven patterns.
• Strong machine learning foundations and the ability to assess emerging methods critically.
• Experience leading technical initiatives across stakeholders and coaching or managing engineers, or clear motivation to take on that responsibility.
• Fluency in English.

Nice to have

• Exposure to graph machine learning, fraud detection, or claims data; French language skills. Experience with agent frameworks such as Microsoft Agent Framework, OpenAI Agents SDK, PydanticAI, or LangGraph is relevant.

Benefits and work setup

Flexible remote and hybrid work options, with location-dependent benefits. The package includes base pay and a variable component tied to individual and company performance, paid time off and holidays, mental health support, learning and development opportunities, and two paid volunteer days each year.