Jobs at Invisible Technologies

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Build and deploy AI-powered solutions for clients by scoping ambiguous problems, designing LLM workflows, integrating data and retrieval systems, and creating scalable production tooling. Collaborate with technical and non-technical stakeholders, iterate based on operator feedback, optimize workflows, and support customer deployments. The role requires strong Python and software or ML engineering skills, production deployment experience, communication abilities, customer on-call availability, travel as needed, and hybrid office attendance.

Build and deploy AI-powered solutions directly with clients and internal delivery teams. Responsibilities include scoping ambiguous problems, designing LLM and retrieval workflows, developing scalable backend and infrastructure systems, optimizing operational workflows, translating messy data into technical architectures, iterating with stakeholders, and creating reusable deployment tooling. The role also involves client communication, end-to-end project ownership, customer on-call support, travel for client engagements, and hybrid office work.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Own strategic planning, workflow automation, compliance operations, systems support, data monitoring, documentation, vendor communications, and cross-functional coordination for a Legal and Compliance team. Translate priorities into roadmaps, improve recurring processes, troubleshoot business systems and data issues, and guide application requirements and QA while maintaining accuracy in a high-pressure environment.

Build and deploy production-grade agentic AI and machine learning systems, including scalable backend services, data pipelines, RAG applications, and cloud infrastructure. Collaborate with ML engineers, data scientists, and clients to discover use cases, develop AI-powered products, optimize platform performance, and support deployments. The role also involves MLOps, model monitoring and versioning, automated testing, infrastructure management, research and development, and direct client engagement.

Own discovery, scoping, delivery, and stakeholder management for AI-enabled data products serving enterprise financial-services clients. Lead workshops, define success metrics, manage timelines and OKRs, coordinate engineering teams, design automation and human-in-the-loop solutions, and maintain executive relationships. The role also requires data analysis, governance, quality, metadata, lineage, playbook development, mentoring, and collaboration with product and sales teams.

Build and deploy AI-powered solutions for client operational problems. Responsibilities include scoping ambiguous requirements, designing LLM and retrieval workflows, developing scalable software, working with messy data, iterating with clients and operators, and creating reusable deployment infrastructure. The role combines AI engineering, software development, and technical consulting, with customer-facing collaboration, on-call responsibilities, and 25–50% travel for client engagements.

Respond to P1/P2 production incidents in a 24/7 rotation, triage infrastructure failures using Kubernetes, RabbitMQ, and PostgreSQL logs, stabilize systems, implement infrastructure fixes, escalate application issues, communicate incident status, and support system hardening and scaling improvements at senior levels.

The Enterprise Solutions Manager drives operational excellence and client success through strategic analysis, process optimization, and project management, collaborating with various teams to deliver AI solutions.

Manage relationships with strategic healthcare clients, oversee complex projects, innovate operational processes, and lead large teams to improve AI training and application.

Own a platform domain end to end, defining architecture, roadmap, reliability, security, and cost standards. Build scalable multi-tenant Kubernetes infrastructure, enable self-service deployments, advance agent-native engineering practices, and develop tooling for safe agent execution. Partner with engineering teams to translate infrastructure needs into reusable capabilities, mentor engineers, lead design decisions, and manage on-call and post-incident processes.

Build and operate the company’s platform infrastructure, including Kubernetes, infrastructure as code, CI/CD, identity, secrets, networking, observability, and developer tooling. Responsibilities include operating production services, supporting agent infrastructure, improving alerting, participating in incident response, and creating self-service capabilities. The role requires strong Kubernetes and cloud experience, Terraform, Helm, GitOps, Python, AI coding-agent expertise, sound engineering judgment, written communication, and comfort with ambiguity.

Leads operational delivery for complex enterprise AI engagements, aligning commercial, engineering, and executive client stakeholders. Owns multi-workstream execution, agile delivery ceremonies, requirements scoping, risk mitigation, KPI and ROI analysis, process design, and business cases. Partners with technical leads on strategic deployments, structures ambiguous challenges into scalable frameworks, advises senior clients, and mentors Enterprise Solutions Managers and delivery teams.

Leads enterprise client engagement, solution design, operational process creation, cross-functional project delivery, workflow optimization, team reviews, staff mentoring, performance analysis, and real-time analytics initiatives. The role bridges enterprise clients, global talent, and internal product, engineering, and operations teams while building scalable processes and supporting the launch of a next-generation marketplace.

Work directly with clients to scope, prototype, and deploy AI-powered workflows. Build scalable systems using LLMs, embedding and retrieval systems, and backend infrastructure. Translate messy data and operational constraints into production-ready solutions, iterate with stakeholders, and maintain deployed systems (including on-call support).

Lead end-to-end AI solutions as a forward-deployed engineer: scope, design, and deploy ML/LLM systems; partner with clients and stakeholders; build infrastructure, data pipelines, and reusable platform components; mentor engineers; own projects from discovery through production and provide on-call support.

Lead high-impact AI solutions, overseeing architecture and deployment. Partner with stakeholders, coach engineers, and drive project success from inception to launch.