Top AI & Machine Learning Jobs

Design, build, evaluate, and deploy multi-step agentic AI systems with planning, tool use, memory, orchestration, and multi-agent collaboration. Integrate LLMs with APIs, databases, and execution environments; create evaluation frameworks for success, reliability, latency, cost, and safety; mitigate prompt injection, hallucination, and unsafe tool use; and collaborate with product, engineering, and research teams on production solutions.

Design, build, evaluate, and deploy multi-step agentic AI systems using autonomous agents, LLMs, tools, APIs, databases, and orchestration frameworks. Develop agent architectures, evaluation frameworks, and production monitoring while addressing reliability, latency, cost, safety, prompt injection, hallucinations, and unsafe tool use. Collaborate with product, engineering, and research teams to deliver intelligent travel-planning experiences.

Designs and operates cloud infrastructure and enterprise AI systems, including service integrations, reusable infrastructure-as-code modules, automation frameworks, policy controls, observability, and AI workflow guardrails. Leads incident resolution, architectural improvements, capacity and cost planning, SaaS integrations, and operational process optimization. Creates technical documentation and engineering standards while mentoring engineers and driving adoption of scalable AI and automation practices across teams.

Design, build, and evaluate multi-step agentic AI systems with planning, tool use, memory, orchestration, and multi-agent collaboration. Integrate LLMs with APIs, databases, and execution environments; develop evaluation frameworks measuring reliability, latency, cost, safety, and task success. Collaborate with product, engineering, and research teams to deliver production-grade solutions, while mitigating prompt injection, hallucinations, unsafe tool use, and other agentic-system risks.

Lead the ML roadmap and build production ML systems for CRM personalization. Own end-to-end modeling, deployment, experimentation, and measurement. Partner with marketing, analytics, and engineering, communicate insights to stakeholders, and mentor other scientists while promoting best practices and reproducibility.

Designs and operates cloud infrastructure and enterprise AI systems, including reusable infrastructure-as-code modules, automation frameworks, SaaS integrations, observability, and policy controls. Leads incident resolution, root-cause analysis, capacity and cost optimization, and operational workflow improvements. Defines secure, scalable AI architectures and guardrails for LLM, RAG, and agentic workflows. Creates architecture documentation, runbooks, self-service resources, and engineering standards while mentoring teams and driving adoption.

Develop and improve production machine learning models for airline pricing and travel marketplace problems. Translate ambiguous business questions into measurable objectives, analyze large datasets, design experiments, apply causal inference, and evaluate model performance. Collaborate with engineers, product managers, analysts, scientists, and Air domain experts to deploy, monitor, and operate reliable AI/ML solutions that improve pricing, commercial decisions, and traveler outcomes.

Lead product direction and build agentic AI systems for autonomous merchandising optimisation: prototype multi-agent flows, take prototypes to production, define trust/governance, drive measurable experiment throughput and conversion uplift, and partner across engineering, data science, and brand teams.

Design, develop, and deploy end-to-end ML models and systems for pricing and revenue optimization. Build data pipelines, feature engineering, model training, evaluation, and production monitoring. Partner with product, engineering, and analytics to define success metrics and run experiments. Apply causal inference and optimization techniques to improve business outcomes and enable scalable model-serving and reuse.

Design, develop, and deploy end-to-end ML models and systems for pricing and revenue optimization. Build data pipelines, feature engineering, model training, evaluation, and production monitoring. Partner with product and engineering to define metrics, run experiments, and apply causal inference and optimization techniques to improve business outcomes.