Top Tech Jobs & Startup Jobs

Lead rapid AI-assisted prototyping for automotive navigation and location-based applications. Build and publish usable mobile apps, validate concepts with users and partners, establish vibe-coding practices, evaluate AI tools, coach engineering and product teams, and advise leadership on AI-driven product development. The role combines software engineering, product thinking, user experience, technical leadership, and responsible use of AI tools.

Lead development and deployment of production-grade internal and customer-facing software, including scalable Big Data pipelines and cloud-native services. Responsibilities include Agile delivery, CI/CD, automated testing, defect analysis, production monitoring, stakeholder demonstrations, backlog planning, and 24/7 product support. The role requires strong Java backend development, Flink and Kafka expertise, REST APIs, AWS and Kubernetes deployment, databases, and DevOps practices.

Execute risk-based IT audit engagements covering SOX ITGCs, application controls, cybersecurity, cloud, privacy, AI governance, and technology operations. Responsibilities include audit planning, risk assessments, control testing, reporting, remediation validation, SOC report reviews, third-party risk assessments, and stakeholder collaboration. The role also contributes to audit methodology improvements, automation, analytics, and governance across emerging technologies.

Design, develop, deploy, monitor, and optimize machine learning and AI solutions across classification, forecasting, NLP, generative AI, and computer vision. Build scalable data pipelines and production-grade AI services, integrate LLM and agentic AI applications, and collaborate with product, engineering, and data teams. Apply model governance, responsible AI, lifecycle management, and cloud deployment practices to deliver reliable enterprise AI solutions.

Build and operate AWS infrastructure supporting AI Hub and CatalystAI. Develop CI/CD pipelines, manage infrastructure as code, deploy Dockerized services, monitor reliability, troubleshoot issues, implement security and secrets management, and support API integrations. Collaborate with development and platform teams to improve deployment workflows, documentation, operational readiness, and automation for AI tooling and developer platforms.

Build and lead production AI/ML components, including embeddings, retrieval, agents, and multimodal systems, for reuse across domain teams. Translate architectural direction into technical specifications and production-ready implementations, make implementation-level decisions, identify scalability issues, and mentor engineers. The role requires deep expertise in representation learning, vector search, retrieval-augmented systems, deep learning, LLMs, agent frameworks, distributed systems, and ML engineering.

Lead the architecture, development, deployment, and monitoring of production-grade machine learning systems for high-definition, ADAS, ISA, and IVI maps. Lead work across computer vision, NLP, deep learning, Generative AI, Agentic AI, MLOps, data ingestion, model training, inference, and scalable cloud or in-vehicle deployment. Provide technical leadership, conduct architecture and code reviews, integrate ML outputs with mapping workflows, and translate business requirements into reliable ML system designs.

Design, develop, and deploy production-grade AI systems using generative AI, LLMs, agentic architectures, retrieval, deep learning, and multimodal models. Build scalable representation and vector-search layers, lead experiments, optimize and evaluate models, and apply responsible AI practices. Collaborate cross-functionally to solve spatial and business challenges, mentor engineers and data scientists, and contribute to patents and publications. The role requires strong Python and ML framework expertise, production MLOps experience, and independent architectural decision-making.

Lead the architecture and delivery of production AI and machine learning platforms, including representation learning, embeddings, vector search, retrieval-augmented generation, foundation models, agentic AI, and multimodal systems. Set technical direction, evaluate emerging technologies, define measurable success criteria, mentor engineers and data scientists, and collaborate with research, engineering, and product teams on scalable, explainable, responsible solutions for spatial and operational challenges.

Lead enterprise AI strategy and delivery, building and scaling LLM-powered applications, agentic systems, RAG/vector retrieval, orchestration, evaluation, governance, and production-grade AI platforms. Partner with engineering, data, cloud, security, and business stakeholders to deploy secure, observable, and scalable AI solutions and drive organizational adoption and transformation.