Top Tech Jobs & Startup Jobs in Los Angeles, CA

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads AI and data solution architecture for client transformation programs. Designs generative AI, data modernization, LLM, vector database, RAG, and LLMOps solutions; develops roadmaps and evaluation frameworks; advises executives; drives innovation; manages workstreams, client relationships, business growth, thought leadership, and team mentorship.

Leads cloud security engagements across IaaS, PaaS, and SaaS environments. Designs security architecture and controls, conducts risk assessments and vulnerability testing, manages monitoring and incident response, tracks remediation, and develops technical deliverables. The manager coaches team members, oversees project quality and timelines, addresses stakeholder issues, and identifies opportunities to improve cloud security through automation and emerging technologies.

Leads corporate deals technology workstreams focused on IT cost and value management. Manages client engagements, analyzes technology spending, develops budgets and forecasts, identifies cost-saving opportunities, and translates findings into recommendations. Coaches teams, manages stakeholders, evaluates technology solutions, and uses digital tools and AI to improve analysis, reporting, and scenario planning.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Provides actuarial consulting support to payer and provider clients through pricing, reserving, forecasting, margin improvement, and cost-savings analyses. Develops healthcare data models and client deliverables, reviews analytical outputs, translates technical findings into recommendations, supports projects from issue definition through presentation, and mentors junior staff. Collaborates across consulting teams to address regulatory, financial, and operational changes in healthcare.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads AWS cloud infrastructure and operations delivery for client environments. Manages project planning, budgets, timelines, architecture, migrations, network deployments, incident resolution, service management, and operational readiness. Reviews technical deliverables, improves operations through automation and AI-enabled workflows, builds client relationships, communicates risks and status, and mentors junior professionals. The role requires at least five years of cloud infrastructure delivery experience, including two years leading enterprise-scale AWS work.

Leads client-facing AWS cloud infrastructure and operations engagements, including platform design, deployment, networking, security controls, migration planning, and modernization. Advises clients on cloud strategy, landing zones, architecture patterns, and solution economics. Oversees complex workstreams, validates technical outcomes, manages delivery risks, communicates with stakeholders, and coaches teams in cloud engineering and operational practices.