Top AI & Machine Learning Jobs

As an AI Engineering Lead, you'll shape AI operations, develop strategies, mentor a team, and integrate AI technologies into operations for impactful outcomes.

The Lead AI Scientist will design and optimize LLM architectures, fine-tune models, develop AI systems, mentor junior members, and collaborate with clients and teams on AI solutions.

Lead AI strategy and client engagements for life sciences clients, drive AI and cloud data strategy, manage and mentor consulting teams, engage C-suite stakeholders, oversee risk and delivery, and build AI-capable practices across the firm.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

The Lead AI Engineer will design and implement LLM-powered applications, optimize RAG pipelines, and evaluate models while integrating AI systems into production environments.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Design, implement, and optimize end-to-end AI pipelines including model training, deployment, monitoring, and inference. Build IaC and CI/CD workflows, ensure secure compliant production deployments, design deep learning models for NLP/vision/speech, troubleshoot performance, and mentor junior AI/Ops engineers while collaborating with stakeholders.

The Enterprise Architect will design scalable AI solutions, mentor teams, support business development, and manage client relationships while evolving technology practices.

The Data Science Manager will lead AI/ML solution development for clients in Supply Chain & Manufacturing, mentor data scientists, and drive business development efforts.

Design, develop, and deploy scalable LLM-based solutions (RAG, agentic workflows, PEFT). Lead architecture, implement cloud and DevOps best practices (AWS, Azure, Databricks, Terraform, Docker, Kubernetes), build prompt/response guardrails, collaborate cross-functionally, and manage full SDLC and production deployments.