Skill areas
Specialist skills for hands-on implementation.
Discuss the expertise your project needs across software engineering, AI, data,
cloud, quality engineering and digital growth.
Forward Deployed Engineering
Forward Deployed Engineers need a blend of deep implementation skill, modern AI stack fluency, and strong client-facing execution.
- Core coding and data: Python, JavaScript, SQL, RESTful and GraphQL integrations.
- AI and modern stack: LLM prompting, RAG pipelines, vector databases, and agent orchestration workflows.
- Cloud and infrastructure: AWS, Azure, or GCP with Docker and Kubernetes deployment support.
- Business-side strength: client communication, requirement translation, and ambiguity handling in fast-moving delivery environments.
Software engineering
Engineering foundations for maintainable applications, dependable integrations and scalable systems.
- Programming languages: Python, Java, JavaScript, C++, Go, and adjacent backend or frontend ecosystems.
- Core engineering depth: data structures, algorithms, performance awareness, and scalable problem solving.
- Databases and APIs: SQL, NoSQL, data modeling, REST, gRPC, and service integration patterns.
- System design and architecture: distributed systems, microservices, high-scale workloads, and resilient application design.
- Modern infrastructure: Git, GitHub workflows, CI/CD, Docker, Kubernetes, and cloud deployment on AWS, Azure, or GCP.
Software automation quality engineering
Test automation across web, mobile, APIs, databases and performance, integrated into the delivery lifecycle.
- Programming and framework design: Java, Python, or JavaScript with reusable automation architecture and strong Git workflows.
- Web and mobile automation: Playwright, Selenium, Appium, dynamic element handling, and cross-browser or cross-device execution.
- API and backend testing: REST Assured, Playwright API workflows, JSON or XML validation, auth handling, and request chaining.
- Database testing: SQL validation, backend data integrity checks, data-driven execution, and setup or cleanup hooks.
- Performance engineering: JMeter, k6, or Gatling with load models, response-time analysis, and bottleneck identification.
AI engineering
GenAI application engineering, prompt engineering, agent workflows, ML engineering, and applied AI roles.
Data science and analytics
Data science, analytics engineering, BI, data engineering, experimentation, and reporting talent.
Cloud and platform
AWS, Azure, DevOps, infrastructure automation, SRE, platform support, and environment reliability roles.
Digital marketing and growth
SEO, paid media, CRM, lifecycle automation, marketing operations, demand generation, and growth support.