Jobs at Technoidentity

Lead the architecture, development, and delivery of distributed workflow-orchestration and production AI solutions. Build AI agents, LLM applications, durable workflows, integrations, and evaluation pipelines while applying distributed-systems, Saga, observability, MLOps, and LLMOps practices. Lead client workshops, translate requirements into architectures and delivery plans, manage technical workstreams, conduct reviews, mentor engineers, and establish reusable engineering standards and capability-center assets.

Build and maintain AI/ML applications, LLM-powered solutions, AI agents, agentic workflows, and Model Context Protocol integrations. Develop prompt, retrieval, tool-calling, and workflow automation systems; evaluate model quality, safety, and reliability; and support production deployment, monitoring, and continuous improvement. Collaborate with product, engineering, and business teams, while documenting architectures and implementation details.

Analyzes solicitation documents, develops compliance matrices and proposal plans, writes and edits proposal content, coordinates SME and cross-functional inputs, maintains proposal libraries, ensures submission compliance, and supports proposal review processes. The role also requires researching solicitations, tracking deadlines, managing multiple bids, and applying basic graphic design skills using proposal and document software.

Design, develop, and execute automation and performance tests for web and API applications using Playwright and JMeter/k6. Perform load, stress, and spike testing, analyze metrics, identify bottlenecks, produce reports and dashboards, and collaborate with dev, DevOps, and business teams to resolve performance issues and recommend tuning.

Build, deploy, and maintain AI/ML and generative AI solutions and platforms. Create CI/CD pipelines, containerized workloads (Docker/Kubernetes), and IaC (Terraform). Support cloud deployments (AWS/Azure/GCP), observability (Prometheus/Grafana), and MLOps practices while collaborating with engineering teams for reliability, performance, and security.

Build end-to-end internal applications and tools: Python backends, React front-ends, APIs, enterprise integrations, and Temporal-based durable workflows. Implement AI workflows and agents for Finance and Supply Chain, own production support (monitoring, incident response), and translate ambiguous business requirements into reliable, well-scoped software with minimal direction.

Lead QA efforts across web and API applications: architect Playwright-based automation frameworks, define performance test strategies with JMeter/k6, integrate tests into CI/CD, mentor QA engineers, manage test delivery and stakeholder reporting, and adopt AI-driven testing practices.