Senior AI Engineering Manager

Jobtailor

Illinois

On-site

USD 180,000 - 260,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

Jobtailor seeks an experienced engineering leader to drive GenAI initiatives and deliver enterprise-scale AI solutions. You will lead cross-functional teams, set technical direction, and ensure alignment with business goals across hybrid cloud and edge environments.

You will own engineering practices, manage resources to meet schedules, mentor engineers, and champion robust, scalable designs. The role requires deep expertise in AI model evaluation, CI/CD, and distributed systems to deliver

Qualifications

Extensive software engineering leadership in AI and software teams.
Proven GenAI solutions and enterprise-scale product delivery.
Experience delivering digital projects in matrixed organizations.
Strong project management, scheduling, and planning skills.
Excellent interpersonal and communication abilities.

Responsibilities

Provide leadership to align team with company goals and deliver AI projects.
Manage resources to ensure on-time delivery and high standards.
Oversee team performance and address training needs.
Establish and supervise engineering best practices.
Own the quality of engineering products and robust solutions.

Skills

AI leadership

GenAI solutions

Cross-functional leadership

Project management

Communication skills

Tools

LangChain

LangGraph

Azure Foundry

SageMaker

Bedrock

EC2

Snowflake

Job description

Providing strong technical support and clear direction to ensure the team is aligned with company goals and capable of delivering advanced AI projects.
Managing resources efficiently to ensure projects are completed on schedule and to a high standard.
Overseeing the performance of both individual team members and the team as a whole, fostering a culture of learning by identifying and addressing training and development needs.
Establishing and supervising the implementation of engineering best practices to maintain consistency and excellence in development processes.
Taking ownership of the quality of engineering products, making sure all solutions are robust, reliable, and meet high standards.

Requirements

Extensive software engineering leadership experience with leading AI and software teams in agile, matrixed organizations
Demonstrated track record of delivering GenAI solutions and enterprise-scale products across hybrid cloud and embedded/edge environments
Experience delivering complex cross-functional digital projects in matrixed organizations
Proven experience with project management concepts including project charters, scheduling and planning projects and successful completion
Excellent interpersonal skills are required to deal with sensitive issues, develop others, or influence others inside and outside the department to take specific actions
Should have strong project management skills, team leadership skills, excellent communication skills, strong analytical and organizational skills
Extensive experience with delivering software solutions at scale and complete grasp of fundamental concepts such as CI/CD, unit testing, integration testing, feature flags, blue/green deployments, canary deployments; experience with incident management
Solid software engineering and distributed systems knowledge; understanding of scalable system design, APIs, microservices, and cloud/edge architectures, enabling effective integration of AI components into production-grade applications
Expertise in enterprise platforms and data ecosystems, hands-on experience with delivering data pipelines and data objects
Working knowledge of major cloud platforms such as Azure, AWS, or GCP, including designing and managing scalable, secure AI workloads using services such as Azure Foundry, SageMaker, Bedrock, EC2, and S3
Experience with Snowflake, including AI compute capabilities such as Cortex.
Deep knowledge of GenAI system design, including prompt engineering patterns such as system prompts, few-shot, chain-of-thought, and structured output; agentic system concepts including tool use, planning, multi-agent orchestration, memory, guardrails; vector databases concepts such as chunking, embeddings; patterns such as RAG (sparse, dense, hybrid); fine-tuning approaches such as LoRA.
Deep knowledge of GenAI model types, including LLMs, SLMs, speech, multimodal, and real-time models
Experience with agentic orchestration frameworks such as LangChain, LangGraph
Familiarity with MCP and A2A protocols
Strong expertise in AI evaluation, model selection, benchmarking and optimization across open-source and commercial model ecosystems.
Strong foundation in AI products evaluation and performance measurement, with deep familiarity with evaluation methodologies, including GenAI-specific metrics (e.g., RAG quality, output reliability), A/B testing, and human-in-the-loop validation to ensure robustness and consistency in production systems.

Core Competencies

Demonstrates extensive leadership in software engineering, particularly in delivering GenAI solutions and managing cross-functional teams within agile environments. Possesses a strong foundation in scalable system design, project management, and AI evaluation methodologies to ensure high-quality engineering outcomes.

Highest-signal resume keywords