Top Tech Jobs & Startup Jobs

Design, train, evaluate, deploy, and maintain computer vision and deep learning systems for industrial applications. Build Azure Machine Learning training and inference pipelines, process large and multispectral image datasets, analyze model errors, and optimize real-time inference for production hardware. Contribute to shared MLOps platforms, tooling, CI/CD, testing, monitoring, and deployment infrastructure while collaborating with engineering and customer teams. Responsibilities also include dataset curation, annotation strategy, production debugging, technical reviews, and mentoring.

Deploys, scales, secures, and maintains cloud platforms and services. Responsibilities include building Azure DevOps CI/CD pipelines, managing Azure infrastructure with Terraform or Bicep, containerizing Node.js and ML services using Docker and Kubernetes, monitoring performance, implementing observability, optimizing cloud costs, and planning networking and disaster recovery.

Lead design, development, and deployment of scalable Java backend systems using the Spring ecosystem. Collaborate with AI/ML teams and stakeholders, define architecture and testing standards, run code reviews, mentor junior engineers, manage production deployments, optimize performance, and promote best practices across projects.

Design, build, and deploy production-ready AI solutions (GenAI, classical ML, computer vision, agentic systems). Translate customer needs into architectures, develop MLOps and evaluation pipelines, deploy scalable backends (Python, FastAPI, Docker), collaborate cross-functionally, and mentor junior engineers.

Lead ML projects as a technical lead: design and implement production-ready ML pipelines and models, mentor junior engineers, translate customer needs into technical solutions, evaluate and integrate ML research, run client workshops, and support pre-sales activities.

Design, build, and operate scalable ML pipelines and cloud infrastructure; productionize data scientists' models; deploy training and inference services; implement CI/CD, monitoring, security, versioning, and IaC; investigate performance and infrastructure issues; advise on tools and best practices.

Serve as client-facing AI consultant: discover opportunities, translate needs into technical goals, build business cases, oversee end-to-end AI projects, coordinate sales/engineering/delivery, ensure ethical/security standards, and support knowledge sharing and client relationships.

Lead and manage end-to-end AI projects: gather and translate client requirements, create backlogs and plans, coordinate resources and timelines, ensure delivery quality, coach and co-lead data science teams, safeguard technical/ethical/legal requirements, and present solutions and ROI to stakeholders.

Lead design, development, deployment, and technical roadmap for scalable, production-grade full-stack applications. Collaborate with AI/ML teams and stakeholders, mentor engineers, enforce code quality, implement CI/CD, testing, containerization, and cloud deployments (Azure/AWS), and drive architecture, monitoring, and performance optimization.

Design, implement, and optimize generative AI models and integrations (GPT, LLaMA, LangChain). Build MLOps pipelines, containerized back-ends (Docker, FastAPI), and cloud deployments (Azure). Collaborate with cross-functional teams, document solutions, mentor colleagues, and keep up with generative AI research to drive customer-facing AI solutions.