Top Tech Jobs & Startup Jobs

Define and govern Java/JVM engineering standards, architecture, tooling, quality guardrails, and competency frameworks across client engagements. Lead technical assessments, mentor engineers, support proposals and client workshops, develop reusable accelerators, and drive adoption of AI-assisted development and LangChain4j-based LLM integrations. Provide technical authority across Java and adjacent backend technologies without owning delivery management or line management.

Build and maintain scalable full-stack applications across React and Next.js frontends, Python and FastAPI backends, PostgreSQL and Redis data layers, containerized environments, and AI/LLM workflows. Responsibilities include integrating OpenAI and Azure OpenAI capabilities, implementing RAG and search flows, debugging distributed systems, writing automated tests, supporting observability and security, and collaborating across frontend, backend, infrastructure, and data teams.

Lead technical business analysis for banking and insurance clients across integration, risk, transformation, and application development initiatives. Elicit and document requirements, create functional specifications, user stories, acceptance criteria, process flows, and models, and support Agile delivery. Collaborate with senior stakeholders, Product Owners, Scrum Masters, architects, developers, and technical teams. Facilitate workshops and sprint ceremonies, manage backlog refinement and prioritization, ensure traceability, and contribute to continuous improvement across consulting engagements.

Build and lead development of a production-grade Python Backend for Frontend service for a React Native application. Responsibilities include evaluating AI-generated code, implementing FastAPI services, integrating OAuth2/OIDC and Azure APIs, managing tokens, aggregating backend responses, enforcing authorization and resilience patterns, and implementing observability, security, CI/CD, and infrastructure improvements. The role also involves mentoring a mid-level developer and collaborating on API versioning and cloud architecture.

Leads the architecture and delivery of Azure- and AWS-based data engineering foundations for data science, machine learning, Generative AI, and agentic AI initiatives. Builds governed pipelines, curated datasets, reusable data products, feature stores, MLOps and LLMOps capabilities, and production-ready AI services. Provides technical leadership to data engineers while coordinating with cloud, security, governance, architecture, and data science teams. Establishes monitoring, deployment, incident management, data quality, privacy, and cloud cost optimization practices.

Design, build, and deploy scalable AI and LLM-driven solutions (RAG, agents). Develop end-to-end ML pipelines, APIs, monitoring, and intelligent document processing while ensuring governance, security, and enterprise-grade production operations.

Own and operate production Kubernetes platforms, manage cluster lifecycle and autoscaling, maintain CI/CD pipelines (Azure DevOps), implement IaC (Terraform), improve observability and SLOs, lead incident response, and mentor engineers to ensure reliability, scalability, security, and cost-efficiency.

Design, develop and maintain C++ and .NET applications; integrate SQL databases; implement CI/CD pipelines for Azure and on-prem; manage Azure DevOps projects; apply DevOps and microservices practices; mentor junior developers; troubleshoot, optimize performance, and ensure code quality and governance.

Design, build, and deploy scalable full-stack applications using .NET/C# backends and Vue/Angular frontends. Implement RESTful APIs and microservices, maintain CI/CD pipelines, support cloud environments (Azure/AWS), conduct code reviews, mentor developers, and contribute to architecture decisions.

The AWS Platform Engineer collaborates with teams to deploy applications, automate processes, and maintain CI/CD pipelines, providing technical leadership and mentorship.