Top AI & Machine Learning Jobs

Build AI-enabled applications, automations, integrations, data workflows, and prototypes across Salesforce, MuleSoft, .NET, RPA, and enterprise platforms. Partner with business and IT stakeholders to identify opportunities, validate measurable value, assess AI and architectural risks, and deliver supportable solutions. Document designs, dependencies, prompts, limitations, runbooks, and handoffs while contributing to exploratory engagements and recommending pivots when initiatives lack value.

Build and maintain production generative AI agents, LLM services, APIs, RAG pipelines, evaluation frameworks, and observability infrastructure. Develop intent recognition, query decomposition, context engineering, and self-service data capabilities. Integrate agents with internal and third-party systems, optimize latency and cost, automate business processes, and support AI-driven media planning and data monetization. Collaborate cross-functionally in an agile environment while adapting to evolving requirements.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

Lead the perception stack for unmanned aircraft, owning architecture, roadmap, team leadership, and hands-on delivery. Develop detection, recognition, and image-space tracking for difficult operational conditions, transition classical computer vision to learned models, define evaluation and performance metrics, and deploy optimized perception software to constrained embedded hardware. Collaborate with state estimation and guidance teams while ensuring latency, reliability, and deterministic fallback behavior.

Lead performance modeling and architecture analysis for next-generation AI accelerators. Build analytical, simulation-based, and trace-driven models; analyze AI workloads and hardware bottlenecks; evaluate architectural features; validate models against RTL, emulation, and silicon data; and provide recommendations that influence hardware and software roadmaps. Partner with architecture, compiler, kernel, runtime, and systems teams to improve latency, throughput, utilization, and energy efficiency.

Leads Lyra Health’s AI/ML engineering roadmap, scales and mentors machine learning teams, and drives production-grade systems for clinical applications. Responsibilities include people leadership, quarterly planning, technical architecture, design reviews, model governance, ML SDLC oversight, and deployment of reliable microservices on Kubernetes. The role partners with product, data science, and clinical stakeholders while ensuring safety, operational excellence, cloud scalability, and compliance for sensitive healthcare data.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Build, deploy, and operate production AI systems for enterprise customers. Responsibilities include developing Python services, designing compound AI workflows with models, prompts, agents, tools, retrieval, and evaluations, monitoring system performance, debugging failures, and improving reliability. The role requires close collaboration with customers and subject matter experts, ownership of production outcomes, pragmatic delivery in ambiguous environments, fluent French communication, and 10–30% travel.

Design and maintain data pipelines, ETL workflows, and graph database models. Build RAG and GraphRAG pipelines for generative AI applications using graph and data platforms. Collaborate with data engineers and scientists, engage client stakeholders, gather requirements, present progress, and translate business needs into technical solutions within a cross-functional consulting team.

Build and modernize enterprise data and AI ecosystems, including real-time streaming pipelines, data products, semantic layers, knowledge graphs, RAG architectures, and GenAI infrastructure. Develop synthetic data workflows, automated data quality systems, and reliable cloud data platforms. Collaborate with architects, DevOps, and stakeholders, mentor engineers, lead prototypes, and apply AI solutions to insurance use cases while ensuring scalability, governance, monitoring, and operational resilience.