Applied AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and manage AI capabilities within an enterprise platform.

• Create agent runtimes, model routing, RAG, tool/function calling, guardrails, evaluation, monitoring, cost management, latency optimization, and production quality controls.

• Implement AI functionalities that perform reliably in production environments instead of just demonstrations.

• Transition AI systems from prototype stages to monitored production settings.

• Conduct thorough evaluations of AI behavior.

• Collaborate with product and engineering teams to assess the appropriate use of AI.

• Transform experiments into production-ready features.

• Integrate evaluation protocols into the delivery process.

• Assist client teams in making informed decisions regarding the genuine value of AI.

• Provide AI capabilities that are effective, dependable, measurable, secure, and mindful of costs.

⛳️ Requirements

• Proven experience in deploying software supported by LLMs, ML models, RAG systems, or agent-based workflows for real production users.

• Experience in designing or executing agent runtimes, orchestration, model routing, tool/function calling, or AI workflow methodologies.

• In-depth knowledge of RAG, grounding, retrieval quality, prompt design, context management, and evaluation methodologies.

• Experience in creating evaluation harnesses, golden sets, regression checks, quality gates, or measurable AI performance frameworks.

• Capacity to regard cost, latency, reliability, and safety as essential engineering constraints.

• Proven experience in establishing guardrails and controls for AI systems, including untrusted retrieved content, tool output risks, and failure modes.

• Experience in monitoring AI behavior in production and enhancing systems based on empirical evidence.

• Strong software engineering capabilities and proficiency in collaborating with backend, platform, product, and security teams.

• Excellent communication and collaboration skills suited for client-facing consulting roles.

• A pragmatic approach with the ability to prioritize business value and deliver useful AI capabilities swiftly and responsibly.

• Familiarity with Claude, GPT, Azure OpenAI, open-source models, model routing, fine-tuning, distillation, or small/edge models.

• Experience with MCP, A2A, multi-agent orchestration, AI tool calling, or agent evaluation.

• Knowledge of LLM-as-judge approaches aligned with human evaluation.

• Experience in semantic knowledge management, ontologies, knowledge graphs, or semantic layers.

• Understanding of AI development lifecycle practices encompassing data, build, evaluation, deployment, monitoring, and continuous improvement.

• Proficiency in using AI-assisted development tools while upholding rigorous review, testing, and safety standards.

🏝️ Benefits

• Remote work.

• Permanent employee position.

• Direct-hire employment.

• Opportunity to collaborate with a global multi-billion dollar software conglomerate.