Scale is a vital part of bringing AI-enabled technologies to the world, from autonomous driving to drones, robots, and large language models. For example, Scale works with the world's top self-driving car and robotics ML teams as well as the largest companies in the generative AI space. 

As our customer base is growing, you will be on the front lines of our field engineering efforts for our federal AI projects, having the opportunity to meaningfully impact millions of dollars in revenue by working closely with our largest public sector customers and ensuring that they become passionate, lifelong Scale customers.

Our Field Engineers ensure customers' experiences with Scale's technology are seamless and optimized to build a successful, long-term partnership. Field Engineers partner closely with Product, Engineering, and Operations to lead existing customers through building production-grade data pipelines. We do this by knowing our customer’s use-cases inside and out, and creating the tooling and documentation needed to scalably support their growth.

Field Engineers in general are experts on Scale's products and codebase, and have general technical knowledge on web development, using APIs, and working with large volumes of data. Field Engineers are strong relationship builders, great project managers, and always fun to work with. 

The ideal person is a software engineer generalist who enjoys working with clients directly, taking ambiguous requirements and issues, and quickly designing and building sustainable solutions in close collaboration with the Product and Engineering teams. This role is at the intersection of customer delivery, product, and engineering, giving you a breadth of experience and fully leveraging both sides of your brain. If this sounds like you, we invite you to apply!

You will:

• Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back
• Work closely with our customer’s engineering teams to optimize data pipelines, including providing input towards designing API endpoints and input/output schemas
• Design, develop and maintain playbooks, internal tools, Scale’s documentation and SDKs to quickly get customers set up for long-term success
• Partner with Software Engineers and Operations to remove any technical hurdles customers may face / Debug technical issues impacting delivery and own technical escalations coming from the customer
• Be accountable for the customer’s technical experience throughout their time with Scale
• Work with AEs and PMs to identify customer-specific feature requests
• Develop technical domain expertise in areas of Large Language Models, 2D Image Annotation, and working with API integrations
• Evangelize Scale and interact with customers at major events
• Have an active TS/SCI clearance

Ideally you'd have:

• Track record of success as a hybrid customer-facing engineer or similar function, wearing multiple hats along the way
• Prior technical hands-on experience working with clients in a pre or post-sales capacity to realize business goals
• Prior experience developing with Python and JavaScript, or other modern software languages
• Strong engineering background preferably in Computer Science, Mathematics, or other quantitative fields
• Strong project management and interpersonal skills
• Presentation skills with a high degree of technical credibility when speaking with executives and front-line engineers
• High level of comfort communicating effectively across internal and external organizations
• Intellectual curiosity, empathy, and ability to operate with high velocity

Nice to haves:

• Experience in AI
• Experience working in classified environments
• Previous experience as a technical go-to-market resource
• Understanding of DevSecOps principles, including secure software delivery, infrastructure as code, and continuous integration/deployment
• Hands-on knowledge of container orchestration with Kubernetes and experience with Helm charts for application deployment
• Experience automating infrastructure and configuration management using Ansible or similar tools

Job Description: 

Accenture Federal Services is seeking a Software Engineer who will develop pipeline development to support a range of government programs in a secure environment. This role focuses on designing, building, software systems and optimizing data pipelines that enable advanced analytics, machine learning, and mission-critical decision support.

Responsibilities:

• Design and develop signal processing software components using Java, and Python
• Define and implement data formatting and interface specifications
• Design, develop and implement algorithms for specialized automated processes and interfaces with databases
• Develop scripts to support remote deployment, troubleshooting and system maintenance activities
• Document software release descriptions and remote deployment procedures

Here's what you need:

• 3 years of experience in software engineering
•  Experience with Java and Python scripting
• Experience with database programming such as SQL, PostgreSQL, or SQL Alchemy
• Experience with any software tools/frameworks such as, but not limited to:Git, Visual Code, Maven, Nexus, etc..

Bonus points if you have:

• Master’s degree in computer science, software engineering, data engineering, data science or a related technical field.
• Experience supporting federal programs
• Familiarity with data lake and data warehouse technologies such as: Snowflake, Redshift, and/or BigQuery
• Knowledge of machine learning model deployment and MLOps practices

Clearance:

• An active TS/SCI federal security clearance is required

The UAS Chief Engineer provides technical leadership, strategic direction, and execution oversight for the design, development, integration, and sustainment of advanced additively manufacturing unmanned systems. This role is responsible for end-to-end system engineering across air, ground, maritime, and/or multi-domain unmanned platforms, ensuring mission effectiveness, survivability, cybersecurity resilience, and compliance with applicable defense standards and regulations. The Chief Engineer serves as the primary technical authority for their assigned platform or product area, guiding cross-functional engineering teams, aligning technical roadmaps with customer requirements, and ensuring delivery of high-performance, secure, and reliable systems within cost and schedule constraints. This position works closely with government stakeholders, program management, operations, and executive leadership.

Essential Functions

• Provide overall technical leadership for additive manufactured unmanned system development programs, from concept through deployment and sustainment.
• Serve as the technical authority for system architecture, design trade studies, integration strategies, and risk management.
• Research and drive advancements in designs centered around additive manufacturing of UAS across the portfolio
• Lead system engineering processes including requirements development, decomposition, verification, validation, and configuration management.
• Oversee multidisciplinary engineering teams (mechanical, electrical, software, controls, AI/ML, autonomy, communications, cybersecurity, and systems engineering).
• Develop and maintain technical roadmaps aligned with defense customer needs and emerging operational requirements.
• Ensure compliance with applicable DoD standards, MIL-STDs, airworthiness/safety certifications, cybersecurity frameworks (e.g., RMF), and export regulations (e.g., ITAR/EAR).
• Direct integration of autonomy, navigation, sensors, communications, propulsion, and payload systems into cohesive, mission-ready platforms.
• Lead major technical reviews (SRR, PDR, CDR, TRR, PRR), ensuring readiness and program maturity at each phase.
• Identify, assess, and mitigate technical risks related to reliability, survivability, resilience, and mission performance.
• Support proposal development, customer briefings, and technical strategy discussions with government and defense partners.
• Foster innovation in autonomy, AI-enabled decision-making, resilient communications, and human-machine teaming.
• Mentor senior engineering staff and establish best practices in additive manufacturing, systems engineering, and product development.
• Perform other duties as assigned.

Standard Essential Functions

• Regular and reliable attendance on a full time basis [or in accordance with posted schedule].
• Responsible for exhibiting professional behavior with both internal and external business associates that reflects positively on the company and is consistent with the company's policies and practices.
• Embodies AEVEX’s cultural values and aligns daily actions with department goals and company culture.

Qualifications and Competencies

• Security Clearance-Ability to obtain/maintain a Top Secret/SCI DoD clearance, U.S. Citizenship required.
• Demonstrated expertise in complex systems engineering for unmanned or autonomous defense systems.
• Extensive knowledge in additive manufacturing of UAS systems; composites, polymers, metals.
• Strong working knowledge of DfX principles; Design for Manufacturability, Procurement, Testability, Modularity, Cost.
• Strong knowledge of platform integration across hardware, embedded systems, software, autonomy, and communications.
• Proven ability to lead large, multidisciplinary technical teams in a regulated defense environment.
• Deep understanding of systems architecture, model-based systems engineering (MBSE), and lifecycle management.
• Familiarity with defense acquisition processes and government contracting environments.
• Working knowledge of cybersecurity requirements for defense systems, including secure communications and resilient architectures.
• Exceptional analytical, problem-solving, and decision-making skills.
• Strong written and verbal communication skills, with experience presenting to senior defense stakeholders.
• Understand of supply chain as it relates to autonomous systems development, rapid production scaling and design for manufacturing.

Education / Certifications

• Bachelor’s degree in Aerospace Engineering, Electrical Engineering, Mechanical Engineering, Systems Engineering, Computer Engineering, or a related technical field required.
• Master’s degree in Engineering, Systems Engineering, Robotics, Computer Science, or related field preferred.

Experience

• 15+ years of progressive engineering experience in defense, aerospace, robotics, or autonomous systems development.
• 10+ years of experience with progressive technical experience in engineering roles, with a proven track record of technical design and development
• 7+ years in senior technical leadership roles (e.g., Lead Engineer, Chief Engineer, Technical Director, or equivalent).
• 7+ years in senior technical engineering roles centered around additive manufacturing principles, programs, and execution
• Demonstrated experience leading at least one full lifecycle unmanned or autonomous system development program.
• Experience interfacing directly with DoD customers, prime contractors, and/or government program offices.
• Background in integrating sensors, navigation systems, propulsion systems, C2 systems, autonomy software, or mission payloads into operational platforms.
• Experience with test and evaluation planning, field demonstrations, and operational deployment support.
• Experience with manufacturing technologies that enable rapid development, test and demonstration of capabilities.
• Experience with safety-critical systems and applicable certification standards.

Physical Requirements

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties the employee is: Frequently required to sit, and to reach to use computers and other office equipment For the purpose of this summary, occasionally is used to represent up to 1/3 of the time given to the work day, frequently represents 1/3 to 2/3 of the time and constantly represents 2/3 or more of the time.

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.

About the role:

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.

Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

What you’ll do:

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system

What success looks like:

• You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
• ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
• Model deployments become more secure, observable, and supportable across real-world mission environments
• The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership

Clearance Requirements:

• Ability to obtain and maintain a Top Secret clearance 

Work Type: 

• Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
• May require up to 40% travel
  

Salary Range: $150,000.00 - $200,000.00

What we will offer you:

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO + 11 paid holidays
• Education & training benefits
• Annual budget for your tech/gadgets needs
• Monthly box of yummy snacks to eat while doing meaningful work
• Remote, hybrid, and flexible work options
• Team off-site in fun places!
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession: 

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there? 

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration. 

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.

About the role:

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.

Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

What you’ll do:

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system

What success looks like:

• You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
• ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
• Model deployments become more secure, observable, and supportable across real-world mission environments
• The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership

Clearance Requirements:

• Ability to obtain and maintain a Top Secret clearance 

Work Type: 

• Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
• May require up to 40% travel
  

Salary Range: $150,000.00 - $200,000.00

What we will offer you:

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO + 11 paid holidays
• Education & training benefits
• Annual budget for your tech/gadgets needs
• Monthly box of yummy snacks to eat while doing meaningful work
• Remote, hybrid, and flexible work options
• Team off-site in fun places!
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession: 

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there? 

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration. 

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

We’re looking for an experienced Principal ML Ops Engineer to support our customers and join our passionate team of high-impact problem solvers.

About the role:

Raft is building mission-critical AI and data platforms for the Department of Defense (DoD). Our systems ingest and process massive volumes of real-time data from hundreds of sensors and operational sources, transform that data into usable intelligence, and deliver it to operators through mission applications and common operational pictures that support time-sensitive decision-making.

Our platform operates at scale, processing billions of events per day with low-latency data pipelines and cloud-native infrastructure. As Raft expands its AI capabilities, we are investing in a more mature end-to-end machine learning platform to support model development, evaluation, deployment, monitoring, and lifecycle management across both cloud and constrained operational environments.

In this role, you will help design, deploy, and mature Raft’s ML platform and MLOps infrastructure. You will work across Kubernetes-based deployment environments, GPU-enabled infrastructure, model serving systems, CI/CD pipelines, and secure production operations to enable rapid and reliable delivery of machine learning capabilities. This role is ideal for someone who understands both the infrastructure needed to run ML systems in production and the practical needs of ML engineers building and deploying models.

What you’ll do:

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

What we are looking for:

• 7+ years of relevant hands-on experience in software engineering, platform engineering, DevOps, MLOps, or related technical roles
• 5+ years of experience with Docker and Kubernetes in production environments
• 5+ years of experience supporting enterprise cloud infrastructure or applications in AWS, Azure, or similar environments
• Strong experience provisioning, operating, and troubleshooting Kubernetes clusters in production
• Experience building and maintaining machine learning platforms, infrastructure, or pipelines used by engineering or data science teams
• Practical experience deploying machine learning workloads on Kubernetes
• Experience managing clusters or workloads that use GPUs
• Strong understanding of Helm and Kubernetes deployment patterns
• Strong scripting or programming skills, preferably in Python
• Experience with modern software engineering practices including Git, CI/CD, DevOps, and Agile/Scrum workflows
• Strong troubleshooting, systems thinking, and communication skills
• Ability to work independently and collaboratively in a fast-moving environment
• Ability to obtain and maintain a Top Secret clearance
• Ability to obtain Security+ certification within the first 90 days of employment

Highly preferred:

• Experience with ML model serving and inference platforms such as Triton Inference Server, KServe, Ray Serve, vLLM, or similar technologies
• Experience with secure and compliant deployment practices in regulated or government environments
• Experience with Kubernetes-based ML platforms such as Kubeflow
• Familiarity with service mesh technologies such as Istio
• Experience provisioning and debugging complex CI/CD systems
• Experience with infrastructure as code tools such as Terraform
• Familiarity with software supply chain security, container hardening, vulnerability management, and runtime scanning
• Experience supporting ML systems across multiple deployment environments, including cloud, on-prem, and edge
• Background working with machine learning engineers on model training, evaluation, packaging, and release workflows
• Familiarity with storage and artifact systems used in ML platforms, such as S3-compatible object stores, registries, and metadata/catalog system

What success looks like:

• You help Raft stand up a more mature and repeatable ML platform for deploying and managing models in production
• ML engineers can move faster because deployment, serving, and platform workflows are clearer, more reliable, and easier to use
• Model deployments become more secure, observable, and supportable across real-world mission environments
• The organization gains stronger infrastructure for model lifecycle management, including deployment standards, runtime patterns, and platform ownership

Clearance Requirements:

• Ability to obtain and maintain a Top Secret clearance 

Work Type: 

• Remote in DMV; McLean, VA; Boston, MA; San Antonio, TX; Colorado Springs, CO; Tampa, FL; Honolulu, HI Locations ONLY
• May require up to 40% travel
  

Salary Range: $150,000.00 - $200,000.00

What we will offer you:

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO + 11 paid holidays
• Education & training benefits
• Annual budget for your tech/gadgets needs
• Monthly box of yummy snacks to eat while doing meaningful work
• Remote, hybrid, and flexible work options
• Team off-site in fun places!
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession: 

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there? 

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration. 

Dark Wolf Solutions is seeking a Senior DevSecOps Engineer to support the developing and sustaining of a cloud-based platform that enables cyber operations across the Department of Defense (DoD). The successful candidates will coordinate and integrate work across legacy platforms. The team provides support to enhance and develop new applications, analytics, and services for the customer and to modernize capabilities through an agile, evolving requirement identification and prioritization process. This position will call for on site support a few days a week in Tampa, FL. 

Required Qualifications:

• Bachelor’s degree in IT Security, Information Systems, or equivalent
• 8 years demonstrated experience providing technical guidance to other engineers on automation/build tools with 3 years of experience supporting platform architecture directly
• Previous experience developing software in an Agile environment
• Extensive knowledge of DevOps practices to include microservices architecture, CI/CD pipelines, cloud architecture, Platform as a Service (Kubernetes or PCF), and infrastructure as code
• Strong hands-on experience with Kubernetes, with a primary focus on Amazon EKS in production, multi-cluster, and secure environments
• Expertise in GitLab CI/CD, including pipeline design, runners, secure builds, artifact management, and promotion workflows
• Deep experience with AWS Cloud services, specifically within AWS GovCloud environments, including IAM, VPC, EC2, EKS, S3, RDS, CloudWatch, KMS, and Security Hub
• Designing architectures that meet FedRAMP, NIST 800-53, and DoD IL2/IL4/IL5 requirements
• Strong GitOps experience using Argo (ArgoCD. / Argo Workflows) for: 
  • Declarative infrastructure and application deployment
  • Multi-environment promotion (dev → test → prod)
  • Automated rollback, drift detection, and auditability 
•  Advanced understanding of secure containerized workloads, including:
  • Image scanning, SBOMs, and vulnerability management
  • Kubernetes RBAC, network policies, and secrets management
•  Experience implementing DevSecOps pipelines, integrating:
  • Static and dynamic security scanning
  • Compliance gates and automated control enforcement
• Proficiency with Infrastructure-as-Code (e.g., Terraform/OpenTofu, Ansible, Helm, Kustomize, or similar) for repeatable and compliant deployments
• Working knowledge of collaboration and workflow tools, including:
  • Jira for agile planning and work tracking
  • Confluence for documentation and knowledge management
  • Mattermost (or similar secure chat platforms) for operational coordination
• Experience operating in regulated or high-security environments, including:
  • Government, defense, or other compliance-driven organizations
• Knowledge of ICD 503 and RMF with previous experience getting Authorization to Operate (ATO) for an application or system
• Understanding of ATO, continuous monitoring, and audit support
• Strong troubleshooting and operational skills across Kubernetes, CI/CD, and AWS cloud infrastructure
• Ability to collaborate with developers, security teams, and program leadership to drive secure, reliable delivery at scale
• Ability to convey complex ideas through conversation, briefings, and writing with clear, concise language
• IASAE Level II (CASP, CISSP, CSSLP) certifications 
• AFMAN 17-1303 certification (Sec+, etc) 
• US Citizenship and ability to be clearable up to the Top Secret clearance with SCI eligibility

Desired Qualifications:

• Experience with Cloud-Based systems and AI/ML Systems, including ALEX 
• Previous experience working in a DoD software factory such as Kessel Run, Platform One, Kobayashi Maru, etc.
• Willingness to submit for a government security clearance
• Certification in cloud architecture with AWS, GCP, or Azure

This position is located in Tampa, FL. The estimated compensation range for this role is $160,000.00 - $180,000.00 commensurate on experience and technical skill set. 

We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.

We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.
 
 In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.

As a Senior MuleSoft Engineer, you will design and deliver API-led integration solutions on Anypoint Platform — directly with clients, from discovery through go-live. You'll drive architecture decisions, protect margin through scope discipline, and build the kind of trusted client relationships that turn one engagement into many.

What You'll Own

You will be the technical owner of your client engagements — not a resource assigned to a ticket queue. That means:

• Client Delivery Ownership: Lead integration programs end-to-end for external clients — from discovery and estimation through SOW shaping, stakeholder alignment, and delivery KPIs (on-time, on-budget, CSAT).
• Stakeholder Management (exec → dev): Navigate the full relationship stack. Translate architecture trade-offs to executives, earn trust with engineering teams, and keep both sides aligned throughout delivery.
• Scope Discipline: Anticipate risk early, communicate trade-offs clearly, and protect margin through change-order discipline — without damaging the relationship.
• Autonomous Execution: Own your calendar, your estimates, and your commitments. No one will chase you for updates. You surface risks before they surface you.

Key Responsibilities

Technical Delivery

• Lead technical design, implementation, and deployment of large-scale MuleSoft integration and API solutions (CloudHub 2.0, Runtime Fabric, Anypoint MQ, Flex Gateway, or on-prem).
• Develop and maintain DataWeave scripts, Mule flows, and CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins).
• Enforce architectural and security standards (RAML/OAS, OAuth2, JWT, TLS, encryption).
• Monitor and optimize integrations for performance, scalability, and maintainability.

Client-Facing Services Delivery

• Lead discovery sessions, shape estimates, and contribute to SOW development — you are the technical voice in the room with the client.
• Own delivery KPIs: on-time rate ≥90%, estimate variance ≤±15%, client satisfaction scores that earn repeat business.
• Proactively communicate status, risks, and decisions — updates go out without being asked.
• Build trusted relationships that make scope conversations easier, not harder.
• When scope creeps (and it will), you call it — with a path forward, not just a flag.

Leadership & Collaboration

• Provide coaching and technical guidance to engineers; lead code reviews and process improvements.
• Operate as a peer, not a manager — lift standards through quality of work and direct feedback.

Innovation & AI

• AI as Your Default Toolchain: You reach for your AI-enabled IDE, LLM-based assistants, and AI-powered static analysis before anything else — not as a shortcut, but because better tools produce better outcomes. You don't write code you can automate reviewing.
• Integration Workflows, Augmented: You actively prototype and deploy AI/LLM services inside integration solutions — summarization, document parsing, intelligent transformation, synthetic test data generation.
• Advance automation with AI-assisted code review and LLM-based development platforms to accelerate delivery and lift code quality across the team.

Ways of Working

• The best engineers here don't wait to be told what to do next. They already know. You set direction, track your own commitments, and communicate proactively — without a layer between you and the client.
• WIP stays at ≤2 concurrent workstreams. You know what's at risk before your teammates do.
• If you've always been three steps ahead of your own deadlines, you'll feel right at home.

Required Qualifications

• Consulting Services Experience: 3–5+ years delivering integration programs for external clients — leading discovery/estimation, shaping SOWs, managing stakeholders from exec to dev, and owning delivery KPIs. This is non-negotiable. Internal IT delivery experience does not substitute.
• Client-Facing Ownership: Proven track record of managing client relationships through the full engagement lifecycle — including difficult scope and change-order conversations that preserve both trust and margin.
• API & Microservices Architecture: Expert in designing, implementing, and maintaining distributed, scalable API-led solutions using MuleSoft and industry-standard patterns.
• MuleSoft Platform Mastery: Advanced hands-on with Anypoint Platform, including DataWeave 2.x, CloudHub 2.0, Runtime Fabric, Flex Gateway, and Anypoint MQ.
• Autonomous Execution: Demonstrated ability to self-govern in flat, PM-free consulting environments — independently scoping, prioritizing, and delivering without hand-holding. If you've only worked in highly structured orgs, be honest with yourself about this one.
• AI/LLM Tools & Automation: You don't just tolerate AI tools — you seek them out. Cursor, LLM-based assistants, AI-powered static analysis — these are in your daily rotation, not your LinkedIn bio.
• DevOps, CI/CD & Automation: Strong experience with CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins), containerization (Docker/Kubernetes), and cloud-native deployment strategies.
• Security & Integration Standards: Advanced knowledge of RAML/OAS, OAuth2, JWT, TLS, and encryption best practices.
• Programming Proficiency: Skilled in Java and Node.js, with the ability to refactor legacy codebases into clean, maintainable solutions.
• MuleSoft Certifications: MuleSoft Certified Developer – Level 1 required; Architect or Integration Professional certifications strongly preferred.

Preferred/Bonus

• Experience integrating Salesforce, AWS, or other SaaS/ERP platforms.
• Familiarity with event-driven architectures (Kafka, Pub/Sub).
• Hands-on experience or strong interest in AI/ML pipeline design or integration.