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Engineer Manager Jobs in Virginia (NOW HIRING)

They are seeking an AI Data Engineer - Manager to lead data architecture and engineering delivery for AI/ML/GenAI solutions, ensuring data integrity and scalability while managing a team and ...

SRE ENGINEER/ MANAGER

Reston, VA · On-site

$59.25 - $78.75/hr

Job Summary (Sr. Manager SRE): - Design, implement, and manage scalable, secure, and fault-tolerant cloud infrastructure using AWS, Azure, or GCP. - Automate infrastructure provisioning and ...

They are seeking an AI Data Engineer - Manager to lead data architecture and engineering delivery for AI/ML/GenAI solutions, ensuring data integrity and scalability while managing a team and ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and ...

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Showing results 1-20

Engineer Manager information

See Virginia salary details

$46.1K

$145.6K

$172.5K

How much do engineer manager jobs pay per year?

As of Jun 24, 2026, the average yearly pay for engineer manager in Virginia is $145,608.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,500.00 and $171,500.00 per year, depending on experience, location, and employer.

What is an Engineering Manager?

An Engineering Manager is a professional who oversees engineering teams, guiding them to achieve project goals and company objectives. They are responsible for managing people, processes, and resources, ensuring that engineering projects are completed on time and within budget. Engineering Managers also collaborate with other departments, mentor team members, and help develop technical strategies. Their role bridges the gap between technical engineering tasks and organizational leadership.

How does an Engineer Manager typically support professional development within their team?

Engineer Managers play a key role in fostering the growth of their team members by providing regular feedback, identifying learning opportunities, and encouraging participation in training or mentorship programs. They often help engineers set clear career goals, guide them through skill development, and advocate for their advancement within the organization. By promoting a culture of continuous improvement and collaboration, Engineer Managers help team members stay motivated and aligned with both personal and company objectives.

What are the key skills and qualifications needed to thrive as an Engineering Manager, and why are they important?

To excel as an Engineering Manager, you need a strong technical background in engineering principles, project management experience, and often a relevant degree such as in engineering or computer science. Familiarity with project management tools (like Jira or Asana), version control systems (such as Git), and possibly certifications like PMP or Agile Scrum Master are typically required. Exceptional leadership, communication, and conflict-resolution skills help you motivate teams and manage stakeholders effectively. These competencies are crucial for delivering successful projects, fostering a productive team environment, and driving organizational goals.

What is the difference between Engineer Manager vs Software Engineer?

AspectEngineer ManagerSoftware Engineer
CredentialsBachelor's or higher in engineering or related field, often with leadership experienceBachelor's or higher in computer science, software engineering, or related field
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersDevelops software, writes code, tests applications
Employer & Industry UsageTech companies, engineering firms, startupsTech companies, software firms, IT departments

Engineer Managers oversee engineering teams, manage projects, and coordinate resources, while Software Engineers focus on designing, coding, and testing software. Both roles require technical skills, but Engineer Managers also handle leadership and strategic planning.

What are the most commonly searched types of Engineer jobs in Virginia? The most popular types of Engineer jobs in Virginia are:
What cities in Virginia are hiring for Engineer Manager jobs? Cities in Virginia with the most Engineer Manager job openings:
Infographic showing various Engineer Manager job openings in Virginia as of June 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $145,608 per year, or $70 per hour.
AI Data Engineer - Manager

AI Data Engineer - Manager

Deloitte

Richmond, VA • On-site

Full-time

Posted 5 days ago


Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

58th of 139 rated financial services


Job description

Job Summary:
Deloitte is a leading consulting firm focused on transforming the nature of work through innovative solutions. They are seeking an AI Data Engineer - Manager to lead data architecture and engineering delivery for AI/ML/GenAI solutions, ensuring data integrity and scalability while managing a team and collaborating with various stakeholders.
Responsibilities:
• Lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption.
• Design and operationalize modern data and retrieval foundations to support LLM-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data.
• Manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring.
• Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
• Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases.
• Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems.
• Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
• Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
• Conduct research to provide technical solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost.
• Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
• Collaborate with Enterprise, Application, Data & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss best design.
• Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
• Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
• Be responsible for the successful execution of AI-powered applications using agile methodology.
• Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
• Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
• Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
• Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
• Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
• Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field.
• 6+ years of consulting experience leading delivery teams, including onshore and offshore team members
• 6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
• 5+ years of experience working in an AI environment
• 5+ years of experience translating requirements into client ready design documents
• 5+ years of experience in software application architecture analysis, design, and delivery
• 5+ years of experience executing full system development life cycle implementations
• Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.
Preferred:
• Advanced degrees such as Masters or PhD are preferred
• Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
• 5 + years of experience in Data Science, Statistics, and Machine Learning
• 5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
• 5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
• 5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure.
Company:
Deloitte drives progress. Our firms around the world help clients become leaders wherever they choose to compete. Founded in 2008, the company is headquartered in Arlington, USA, with a team of 10001+ employees. The company is currently Late Stage.

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