Top Tech Jobs & Startup Jobs

The DevOps Engineer will manage cloud-based infrastructure, automate CI/CD processes, and ensure system reliability while collaborating with an IaaS provider.

Lead the design and deployment of AI/ML solutions, managing multiple initiatives and mentoring technical teams to ensure production-ready models.

Lead the deployment and management of ML/DL models, design systems for dataset management, and optimize model serving processes in production.

The Senior Full Stack Developer will lead the frontend initiatives while delivering features across the stack, focusing on AI-powered solutions for businesses.

Design and build scalable AI-powered applications and distributed systems in Python. Develop AI agents, RAG solutions, and MCP integrations; design cloud/hybrid/on-prem architectures, implement real-time communication (WebSocket/WebRTC/HTTP), create documentation and diagrams, and collaborate with clients on requirements, demos, estimation, and planning.

Lead front-end BI strategy, develop advanced Power BI solutions, manage data models and security, integrate AI features, and collaborate with stakeholders.

Design and implement streaming and batch data pipelines, perform feature engineering and data exploration, build Gold data layer and KPIs, collaborate with business teams to standardize sensor data, and establish production-ready engineering practices (CI/CD, IaC, testing, documentation) for an anomaly detection PoC.

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.

Research and implement AI tools to improve business processes; design, develop, and maintain .NET applications; support Microsoft Dynamics and Dynamics AX integrations; assist with AWS cloud services; troubleshoot and improve existing systems; collaborate with stakeholders and cross-functional teams to deliver solutions.

Design and build scalable agentic workflows and microservices (primarily Python). Integrate generative AI, LLMs, and RAG into production. Implement observability, autonomous monitors, AI-SDLC practices, and mentor engineers while driving engineering quality.