Applied AI Engineer

Deriv is an AI-first company, transforming trading through cutting-edge technology. We are seeking a highly skilled AI Engineer to join our dynamic team. You will play a key role in designing, developing, and deploying AI-driven solutions that power our internal tools, marketing automation, and security infrastructure.

Your challenges

AI Solution Development

. Design, build, and operate agentic LLM applications with tool-use/function calling, planning, memory, and structured outputs with versioned prompts.

. Implement RAG pipelines (hybrid dense+sparse retrieval, reranking) manage vector indices and the embedding lifecycle.

. Integrate models with internal services and third-party APIs via tool adapters enforce schema contracts and access controls.

. Lead feature work in recommendation/ranking, content generation/editing, and workflow orchestration backed by measurable eval metrics.

Security & Automation

. Build AI-driven security automations: classifiers/anomaly detectors, red-teaming harnesses, and (where appropriate) autonomous testing agents.

. Implement safety & privacy controls: prompt-injection/jailbreak defenses, PII redaction, policy filtering, and auditability.

. Stand up real-time tracing & alerts on token/latency/cost, retrieval degradation, drift, and hallucination risk.

Project & Task Management

. Own model/prompt/agent versioning and ship via CI/CD with canaries/A-B, rollback plans, and clear SLOs (quality, latency, cost).

. Evolve workflows: PR-based changes to prompts/configs, experiment tracking, concise design docs, and ADRs (decision records).

Quality Assurance & Code Review

. Enforce evaluation-first development: golden sets, synthetic probes, regression tests for prompts, grounding/faithfulness checks.

. Review for determinism, observability, and reproducibility address tech debt with a bias to stability and measured impact.

Collaboration & Mentorship

. Partner with platform/data teams on data contracts, retrieval standards, and tenancy/access control.

. Mentor peers on agent patterns, RAG, prompt engineering, and safe rollout lead design reviews and share practical research findings.

Continuous Improvement

. Benchmark and integrate new model families/tools via standardized bake-offs report quality-latency-cost trade-offs.

. Optimize serving with adapters/LoRA, distillation/quantization, routing/caching/speculative decoding right-size infra for cost/perf.

. Track industry and research trends convert into pragmatic, incremental stack upgrades.

Requirements

. Bachelor's or Master's degree in Computer Science, Engineering, or related field.

. Strong programming skills in Python, JavaScript/TypeScript, or similar languages.

. Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face).

. Hands-on experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).

. Solid understanding of security best practices and experience with security automation is a plus.

. Excellent problem-solving, communication, and leadership skills.

. Experience with task management and code collaboration tools (GitHub, Jira, ClickUp, etc.).

. Ability to work independently and as part of a fast-paced, collaborative team.

Nice to Have

. Experience in AI/ML engineering, with a proven track record of delivering production-grade solutions.

. Experience with marketing automation, content generation, or design systems.

. Familiarity with Figma, Supabase, or similar platforms.

. Contributions to open source AI/security projects.

. Prior experience in a high-growth tech or fintech environment.