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Deployment Engineer Jobs in Dallas, TX (NOW HIRING)

Lead Full Stack Engineer

Dallas, TX · On-site

$101K - $133K/yr

The Lead Forward Deployment Engineer will work across care navigation, benefits, and authenticated digital channels to understand business workflows, translate ambiguous needs into technical ...

Lead Full Stack Engineer

Dallas, TX · On-site

$101K - $133K/yr

The Lead Forward Deployment Engineer will work across care navigation, benefits, and authenticated digital channels to understand business workflows, translate ambiguous needs into technical ...

... Engineer with a strong focus on change and release management, building and maintaining robust CI ... The position requires expertise in automating infrastructure and deployment processes to ensure ...

Showing results 21-40

Deployment Engineer information

See Dallas, TX salary details

$35.1K

$108.4K

$168.2K

How much do deployment engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for deployment engineer in Dallas, TX is $108,427.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,700.00 and $137,100.00 per year, depending on experience, location, and employer.

What is a deployment engineer?

A deployment engineer is a computer system specialist who installs and maintains networks, software, or computer systems. As a deployment engineer, your responsibilities include troubleshooting issues related to routers and wireless networks, training customers how to use methods or implement upgrades, and ensuring that security is functioning properly on all network assets. Qualifications to become a deployment engineer include proficiency with networks protocols, proprietary programs, and equipment. Many employers prefer candidates with customer support experience.

What are the key skills and qualifications needed to thrive as a deployment engineer?

To thrive as a Deployment Engineer, you need a solid background in software development, systems administration, and deployment methodologies, often supported by a degree in computer science or related field. Familiarity with configuration management tools (like Ansible, Puppet, or Chef), CI/CD pipelines, and cloud platforms such as AWS or Azure is typically required. Problem-solving, attention to detail, and strong communication skills distinguish top performers in this role. These skills are crucial for ensuring smooth, reliable software releases and effective collaboration with cross-functional teams.

What are some common challenges faced by deployment engineers during software rollout, and how are they typically addressed?

Deployment Engineers often face challenges such as coordinating with multiple teams, managing unexpected technical issues during rollout, and ensuring minimal downtime. These are typically addressed by thorough planning, automated deployment pipelines, and clear communication with stakeholders. Proactive testing in staging environments and having rollback strategies in place also help mitigate risks and ensure smooth deployments.

What is the difference between Deployment Engineer vs Network Engineer?

AspectDeployment EngineerNetwork Engineer
Required CredentialsBachelor's in CS or IT, certifications like Cisco CCNA, CompTIA Network+Bachelor's in CS, IT, or related field; Cisco CCNA, CompTIA Network+ often preferred
Work EnvironmentData centers, client sites, cloud environmentsCorporate offices, data centers, network operation centers
Industry UsageIT services, cloud providers, telecomTelecommunications, enterprise IT, service providers
Common Search/ComparisonDeployment Engineer vs Network Engineer

Deployment Engineers focus on implementing and configuring software or hardware solutions across various environments, ensuring smooth deployment processes. Network Engineers specialize in designing, maintaining, and troubleshooting network infrastructure. While both roles require networking knowledge and certifications, Deployment Engineers often work closely with software and system deployment, whereas Network Engineers focus on network connectivity and security.

What are the most commonly searched types of Deployment Engineer jobs in Dallas, TX?

The most popular types of Deployment Engineer jobs in Dallas, TX are:

What are popular job titles related to Deployment Engineer jobs in Dallas, TX?

For Deployment Engineer jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Deployment Engineer jobs in Dallas, TX look for?

The top searched job categories for Deployment Engineer jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Deployment Engineer jobs?

Cities near Dallas, TX with the most Deployment Engineer job openings:

Infographic showing various Deployment Engineer job openings in Dallas, TX as of August 2026, with employment types broken down into 92% Full Time, 4% Part Time, and 4% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $108,427 per year, or $52.1 per hour.

Principal Forward Deployment Engineer (FDE)

Accellor

Dallas, TX

Full-time

Medical, Life, Retirement, PTO

Posted 7 days ago


Job description

Accellor is an AI-native services firm purpose-built for the post-ChatGPT era. Free from legacy constraints, we focus on delivering measurable business outcomes through advanced AI, data, and engineering capabilities. Our mission is to operationalize AI at scale and unlock sustained enterprise value. 

Our offerings span AI solutions, data services, enterprise applications, and product engineering, tailored to industry-specific needs across healthcare, life sciences, telecom, retail, financial services, and technology. By leveraging design thinking and technology-agnostic architectures, we ensure faster time-to-value and seamless interoperability. 

With a proven track record of enabling Fortune 100 enterprises and global innovators, Accellor stands as a trusted partner for organizations seeking to harness the full potential of AI. Our vision is clear: to build intelligent, connected ecosystems that deliver measurable outcomes and redefine the future of enterprise transformation. 

About the role:
As a Principal FDE, you'll be the senior technical leader inside our most strategic enterprise engagements. You'll embed with customers to translate AI ambitions into production architectures, designing how data, AI, and governance fit together end-to-end. You'll be the technical voice the customer trusts and the field-level expert whose patterns shape what we build next in product.

You'll lead FDE through high-stakes, ambiguous customer deployments and own technical and business value outcomes end to end. You'll grow a team that can operate under pressure and help our organization learn from the field.

The FDE works directly with strategic customers and designs how AI, data and governance come together end-to-end to ensure deployments are scalable, secure, and deliver measurable outcomes. This role goes beyond solution design. By shaping architectures, defining best practices, and identifying product gaps in real time, the Architect directly influences the evolution of the platform. Their work creates patterns that accelerate future deployments, strengthen technical credibility with enterprise buyers, and reduce time to value across engagements.

You'll partner closely with Product, Research, Sales, and GTM to ensure fieldwork informs roadmap priorities, drives new exploration, and supports safe deployment at scale. Your decisions will influence how we are trusted by the customers closest to our deployment work. Your success will be measured by how consistently your team ships, how clearly you deliver signal to Research and Product, and how durable your team and delivery model prove to be.

In this role you will:

  • Run technical discovery workshops with customer architects, data leaders, and AI teams, mapping data sources, MCP Workspace scoping, and agent tooling requirements
  • Own the scoping for AI deployments with clear acceptance criteria around agent accuracy, data coverage, and governance
  • Translate complex AI + data concepts into executive-ready architecture proposals; defend trade-offs to CxO-level stakeholders
  • Lead and grow a team of FDE delivering production systems with frontier models
  • Own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality
  • Own the technical solution end to end, from customer discovery and workflow scoping through architecture, hands-on implementation, evaluation, production deployment, adoption, and handoff
  • Partner credibly with customer engineers, operators, and domain experts to frame ambiguous problems, define scope, and translate business workflows into technical requirements and measurable outcomes.
  • Design solutions across various AI sources covering MCP server configuration, semantic context modeling, and governance integration
  • Architect agent orchestration, defining which data, schemas, and actions each agent can access in production
  • Design governed access patterns for AI agents: RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements
  • Define AI Best practices using agents, skills and LLMs to drive successful customer outcomes
  • Identify architectural and product gaps during live enterprise engagements and partner with Product and Engineering to define scalable solutions
  • Author technical specifications and implementation recommendations for enhancements, including both features and core architectural improvements
  • Build reusable reference architectures, deployment patterns, and MCP blueprints that reduce implementation friction and accelerate future customer deployments
  • Translate recurring customer deployment challenges into scalable platform capabilities and architectural standards
  • Codify what works into tools, playbooks, and roadmap inputs that create leverage for our enterprise customers
  • Notice early indicators and raise them with urgency, whether in product behavior, customer environments, or delivery practices
  • Use judgement to distinguish what requires action and what does not
  • Set a high bar for FDE performance and support each person's growth through direct, actionable feedback
  • Define how we staff and support field teams that can scale without added complexity

Requirements

You might thrive in this role if you:

  • Bring 15+ years of engineering or technical delivery experience, including 2+ years managing high-performing FDE or customer-facing engineers
  • 7+ years as a Solutions Architect, Principal SE, Forward Deployed Engineer, or Technical Lead at a data platform, AI, or enterprise SaaS company
  • Customer-facing track record with senior technical buyers and architecture review boards
  • High agency; comfortable being the senior technical voice in the room with the customer
  • Ability to translate complex AI + data concepts into executive-ready architecture proposals
  • Has built and shipped production AI applications, not just prototypes
  • Worked on a SaaS Platform in an Architect Profile (or closely aligned role)
  • Have led high-pressure technical projects from prototype to production
  • Write and review production-grade code across frontend and backend using JavaScript or Python
  • Have built or deployed systems powered by LLMs or generative models and understand how model behavior affects product experience
  • Simplify complex work and make fast, sound decisions under pressure
  • Elevate team performance through clarity, not process
  • Operate with urgency in ambiguous or evolving environments
  • Translate field experience into sharp, actionable feedback for Product and Research
  • Build deep trust with your team by modeling calm, focus, and judgment when it matters most
  • Strong AI/ML literacy: LLM capabilities, agentic architectures, RAG patterns, prompt engineering, and when to apply each
  • Able to define Multi-tenant Architectural patterns and security objectives
  • Hands-on enterprise data integration: SQL, ETL/CDC pipelines, API design, ODBC/JDBC, and multi-source connectivity
  • Able to design governed data access for AI agents, RBAC, OAuth 2.1, semantic scoping, and audit-trail requirements
  • Experience with modern data stacks (Snowflake, Databricks, Salesforce) and cloud-native deployment patterns
  • Experience mentoring junior engineers without requiring direct reporting relationships

Nice to Have:

  • Direct experience with the MCP protocol and AI agent frameworks (LangChain, CrewAI, Copilot Studio)
  • Prior experience as an FDE or in a similar embedded customer-facing engineering role

Benefits

At Accellor, we believe in equitable compensation. The base salary for this position will be in the range of $200,000 to $225,000 with additional revenue-linked performance incentives. The actual pay will depend on your skills, experience, and qualifications. The salary range is subject to change.  

In addition, we offer: 

Work-Life Balance: Accellor prioritizes work-life balance, which is why we offer, flexible work schedules, opportunities to work from home, and paid time off and holidays.  

Financial and Medical Benefits: Our package includes perks like flexible and discretionary time off, healthcare coverage for you and your loved ones, and a retirement plan to help you plan for the future. Additionally, we offer access to flexible spending and health savings accounts, life and AD&D insurances. 

Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training, Stress Management program, professional certifications, and technical and soft skill trainings. 

Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them. 

Collaborative Environment: You can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment - or even abroad in one of our global centres. 

Accellor is proud to be an equal-opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristicsÂ