Top Tech Jobs & Startup Jobs

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.

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.

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.

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.

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 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.

Lead end-to-end talent acquisition, onboarding, and culture efforts for an AI engineering firm. Build recruitment pipelines, define hiring scorecards, improve selection and candidate experience, design onboarding and growth paths, run culture and knowledge-sharing initiatives, and partner with marketing on employer branding.

Lead the design, implementation, and scaling of generative and agentic AI systems (RAG, LLM fine-tuning, embeddings). Oversee MLOps pipelines, containerised back-end services (Docker, FastAPI), mentor engineers, drive Evaluation-Driven Development, integrate third-party solutions, and collaborate cross-functionally to deploy robust, production-grade NLP and GenAI products.

Design, prototype, and productionize applied AI systems (RAG, copilots, agentic workflows, fine-tuned models). Build reproducible evaluation and experimentation pipelines, containerized back-ends, and collaborate with engineers and customers to improve model quality, latency, and cost-efficiency. Document and share findings, mentor peers, and translate research into customer-facing solutions.