1 day ago
Base Salary
$92k - $170k/yr
Responsibilities
- Own defined solution components or technical workstreams from problem discovery and design through implementation, first production deployment, and handover.
- Translate client priorities into technical requirements, engagement scope, business outcomes, access needs, and success criteria.
- Design and build solution components spanning data, integrations, domain modeling, agent orchestration, evaluation, operations, security, and governance.
- Build and integrate APIs, data pipelines, MCP servers, ontologies, knowledge bases, agent workflows, reusable skills, and operational tooling.
- Apply evaluation, regression testing, observability, human approval, security, responsible AI, identity, audit, data residency, sovereignty, and regulatory controls.
- Support technical workshops, communicate solution tradeoffs and progress, collaborate with FDE POD members and specialists, and support less experienced engineers.
- Contribute reusable assets, field feedback, proposals, estimates, solution shaping, technical risk tracking, and handover readiness.
Requirements
- Degree in computer science, engineering, information technology, or a related field, or equivalent practical experience.
- Typically 5 to 8 years of overall experience, including at least 3 years of hands-on cloud, data, software, or AI engineering experience.
- Demonstrable experience in software, data, cloud, platform, or AI engineering within a large enterprise, product organization, or technology services environment.
- Experience building or contributing to production systems integrating enterprise data, applications, security controls, and cloud services.
- Experience owning technical components or workstreams across design, build, testing, deployment, production readiness, handover, and client enablement.
- Experience with agentic systems, tool schemas, context and memory, model selection, RAG, MCP servers, knowledge bases or knowledge graphs, and agent integrations.
- Working knowledge of evaluation engineering, AgentOps, observability, regression testing, production reliability, human-in-the-loop controls, enterprise integration, APIs, event-driven architectures, data platforms, and legacy systems.
- Experience with Git, infrastructure as code, containers, automated testing, and cloud operations.
- Strong client engagement, communication, independent decision-making, product mindset, and collaborative delivery skills.
- AWS Certified Cloud Practitioner, AWS Certified Solutions Architect Associate, and AWS Certified AI Practitioner are expected baseline credentials for the AWS deployment path; AWS Certified Generative AI Developer Professional is the target credential, while Machine Learning Engineer Associate or Data Engineer Associate are advantageous.
- Equivalent Azure, Google Cloud, or other platform certifications are valued and may be required for other deployment paths.
- Experience supporting opportunity qualification, solution shaping, effort estimation, or proposal development is desirable.
Benefits
- Remote U.S.-based role with hybrid working designation.
- Compensation range is provided for U.S.-based positions and varies by location, experience, skills, and qualifications.