Top AI & Machine Learning Jobs

Lead design, development, validation, and deployment of simulation and ML/GenAI models for strategic insurance decision-making. Perform scenario and risk analysis, translate business problems into simulation frameworks, interpret large datasets, advise executives, mentor junior staff, and partner with governance, legal, IT, and business stakeholders to ensure model rigor, compliance, and actionable recommendations.

Design, build, and operate AI-native, agentic cybersecurity systems at enterprise scale. Architect end-to-end AI features, integrate models, implement orchestration, ensure reliability, scalability, observability, and cost efficiency, and collaborate with product, data science, and security research teams. Mentor engineers and support full lifecycle from design through production and continuous improvement.

Own product vision and multi-year strategy for M365 Fleet Infrastructure to ensure availability, durability, reliability, performance, and efficiency at hyperscale. Drive roadmap, KPI/SLA definitions, capacity modeling, provisioning/decommissioning, Azure-based modernization, and cross-group alignment to support Copilot/AI workloads. Translate complex platform needs into executive-facing investment narratives and measurable outcomes.

Build and optimize scalable, reliable training infrastructure and services for LLMs, SLMs and agentic models. Collaborate with researchers and engineers to design production APIs, manage cloud and Kubernetes-based platforms, ensure security and observability, deploy and monitor services, and drive engineering best practices and incident response.

Own applied computer vision and machine learning model development from problem definition through production deployment. Build and optimize solutions for object detection, segmentation, classification, video analysis, forecasting, anomaly detection, regression, and clustering. Evaluate architectures using PyTorch and TensorFlow, analyze performance, and account for cameras, sensors, lighting, and edge-device constraints to ensure robust real-world results.

Author and QA AI-driven automations for defined healthcare workflows; execute high-volume repetitive automation tasks accurately; maintain structured technical and process documentation; provide platform support; collaborate with product, engineering, and QA teams using GitHub, Slack, and JIRA; contribute to continuous improvements of core applications and developer tooling.

Evaluates English-to-Turkish machine-translated and AI-generated software content for accuracy, fluency, terminology, formatting, and usability. Identifies translation errors, validates localization against style guides and terminology, reviews UI strings and technical documentation, and provides feedback to improve MT and LLM output quality. Collaborates with localization managers and project teams as a freelance linguist on remote technology projects.

Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Drive architecture choices (attention variants, MoE), optimize training stability and optimizers, validate scaling laws and compute/data tradeoffs, design loss and alignment strategies (RLHF, DPO, contrastive), and execute distributed training (FSDP, ZeRO-3, tensor/pipeline parallelism) with mixed precision and checkpointing while partnering with ML systems teams.

Build, validate, deploy, and monitor credit-scoring models for application and behavioral risk. Translate models into implementation specs, measure business impact (NPV, backtesting), and collaborate with product, analytics, and engineering to refine credit decisioning and portfolio performance.

Build and own application and behavioral credit scoring models across the full lifecycle: data exploration, feature engineering, model development, validation, deployment, and production monitoring. Validate using AUC/KS/Gini/PSI, measure business impact (NPV, backtesting), translate models into decision-engine specs, and collaborate with product, engineering, and risk to refine approval strategies and portfolio performance.

Develop and modernize production software using AI-assisted development tools and sound engineering practices. Build AI agents and agentic workflows, analyze legacy code, create automated tests, review AI-generated code, and apply secure coding, CI/CD, observability, and documentation standards. Collaborate with architects, product owners, stakeholders, and developers to deliver applications across enterprise, cloud-native, API, and modern user-experience environments. Contribute reusable AI engineering patterns and responsibly evaluate emerging development technologies.

Design, build, and operate scalable ML pipelines and infrastructure across cloud and on‑prem HPC. Implement MLOps tooling (tracking, registries, feature stores), CI/CD/CT, containerized GPU orchestration, model optimization (LLMs, GNNs, RL), data/versioning pipelines, monitoring, and mentor engineers to productionize ML for EDA and simulation workloads.

Adapt, integrate, and validate quantum and quantum‑enhanced machine learning methods for molecular and chemical client use-cases. Build end-to-end ML pipelines, select molecular representations, integrate with PASQAL QPUs and simulators, collaborate with R&D and chemistry experts, and produce reproducible code, documentation, and benchmarks.

Designs enterprise AI-first, predictive, and autonomous managed-services solutions across business and IT operations. Identifies automation opportunities, architects AI agents, multi-agent workflows, RAG, and self-healing systems across enterprise platforms and AWS. Establishes governance, security controls, observability, compliance, and human oversight. Leads stakeholder workshops, proofs of concept, and innovation initiatives while defining performance, ROI, efficiency, and cost metrics.