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Experience
1 - 7 Years
Job Location
Education
Bachelor of Technology/Engineering
Nationality
Any Nationality
Gender
Not Mentioned
Vacancy
1 Vacancy
Job Description
Roles & Responsibilities
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
Desired Candidate Profile
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.
- 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.
Company Industry
- Banking
- Financial Services
- Broking
Department / Functional Area
- IT Software
Keywords
- Applied AI Engineer
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Deriv
Deriv is an AI-first company, transforming trading through cutting-edge technology. We are seeking a highly skilled Applied 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.
AI is being embedded into our company DNA, across every department. For further information, please read our guide: AI at Deriv:How we re transforming fintech with AI.
https://deriv.zohorecruit.eu/jobs/Careers/47807000086591033/
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