Top Tech Jobs & Startup Jobs

Build and operate production-grade machine learning infrastructure, including automated training and deployment pipelines, model serving, monitoring, observability, retraining, and governance. Deploy ML and LLM systems across cloud, on-premises, and hybrid environments, while collaborating with data scientists and software engineers. Support CI/CD, model registries, security, reliability, GPU utilization, and RAG infrastructure.

Design and develop scalable AI workflows and models in Python, focusing on LLMs and machine learning. Collaborate with data scientists, developers, and domain experts to translate business needs into practical solutions. Research emerging AI techniques, build and deploy models, document reproducible work, and communicate results clearly. The role requires experience with ML frameworks, LLM applications, vector databases, cloud platforms, APIs, and software development practices.

Design, build, and operate scalable data platforms and ETL pipelines supporting analytics, reporting, customer insights, campaign measurement, and AI/ML initiatives. Develop data solutions using Python, SQL, Spark, cloud platforms, and modern data tools. Ensure data quality, governance, security, lineage, and observability while troubleshooting production pipelines and collaborating with technical and business stakeholders.

Architects and builds production-grade GenAI systems, including RAG pipelines, LLM integrations, agent workflows, evaluation frameworks, guardrails, and monitoring. Optimizes inference cost, latency, reliability, and quality while developing reusable AI components and services. Leads the transition from prototypes to production, mentors junior engineers, and manages one to two engineers.

Design, build, and maintain scalable data science and machine learning solutions for marketing services in financial services. Responsibilities include defining business problems, analyzing large and messy datasets, developing supervised and unsupervised models, building ML pipelines, improving existing solutions, and communicating insights to stakeholders. The role partners with Product, Engineering, Data Science, and business teams to deliver secure, reliable, enterprise-scale analytics and AI initiatives.

Build, operate, and improve cloud and on-premises Kubernetes platforms supporting applications, data platforms, and AI workloads. Responsibilities include cluster administration, Helm and GitOps deployments, Azure AKS infrastructure, monitoring, troubleshooting, CI/CD, infrastructure automation, security controls, production support, incident resolution, and documentation. The role collaborates with software, data, AI, and senior cloud engineers while progressively owning larger infrastructure initiatives.

Lead and mentor Cloud Engineers while owning the technical direction, roadmap, and delivery of cloud and platform infrastructure. Architect and operate Azure, Kubernetes, hybrid, and on-premises environments supporting production, data, and AI workloads. Drive Terraform-based infrastructure automation, GitOps, CI/CD, observability, security, resilience, and incident response. Collaborate with Software, Data, and AI Engineering teams on scalable infrastructure, including GPU and data-intensive workloads.

Design, build, and operate scalable batch, streaming, and real-time data platforms for marketing analytics and AI/ML products. Develop ingestion, transformation, orchestration, and delivery frameworks across cloud data environments; improve scalability, reliability, security, governance, observability, and lineage. Lead cloud modernization, CI/CD and infrastructure automation, resolve complex production issues, influence architecture, mentor engineers, and collaborate with product, engineering, data science, and business stakeholders.

Integrate and extend a modular simulation-optimisation framework with AnyLogic production models and plant data. Develop Java components to run scenarios, validate I/O and KPIs, apply optimisation methods for scheduling and multi-objective problems, quantify operational benefits, and support operationalisation through dashboards, documentation, and stakeholder collaboration.

Support design and development of ETL pipelines, assist with data migrations and cloud transitions, troubleshoot data processes, and collaborate with architects, developers, and analysts while learning cloud and data engineering tools.