Jobs at Evolver (evolver.ai)

Design, develop, and deploy scalable knowledge graphs and graph-based systems connecting structured and unstructured data. Build ingestion pipelines, entity resolution, graph retrieval, and AI integrations including GraphRAG and vector search. Optimize graph queries, indexing, reliability, monitoring, access controls, and recovery. Develop production-grade Python services, APIs, and pipelines while collaborating with AI engineers and domain experts.

Leads a small engineering team building the AI platform’s planning and execution layer. Owns roadmap, architecture, orchestration, workflow reliability, monitoring, and production delivery. Develops Python systems for goal decomposition, agent and tool coordination, persistent state, retries, concurrency, adaptive replanning, human oversight, and AI evaluation. Partners with product, domain, infrastructure, and security teams while balancing hands-on engineering with technical leadership.

Build and ship AI-powered enterprise web applications across the full stack. Develop responsive frontend experiences with Vue or React, backend services in Python, REST API integrations, asynchronous workflows, streaming updates, and long-running task handling. Own features from technical design through production release, improve application architecture and reliability, write tests and instrumentation, and collaborate with product, design, domain, platform, infrastructure, and security teams.

Lead a small team developing production web applications on an AI platform. Own the application roadmap, architecture, technical direction, and delivery while building responsive frontend experiences and backend integrations. Translate business needs into user workflows, integrate REST APIs and AI services, handle streaming and long-running operations, and establish testing, observability, and evaluation practices. Coach engineers and collaborate with product, design, domain, platform, infrastructure, and security teams.

Leads end-to-end customer contracting, including drafting, redlining, and negotiating MSAs, SOWs, order forms, amendments, and NDAs. Manages vendor onboarding, due diligence, security questionnaires, RFP/RFI responses, contract governance, approvals, and obligation tracking. Partners with Legal, Sales, Product, Engineering, and Delivery teams while advising on commercial operations tools and prioritizing contracting workstreams.

Develop research methods for autonomous systems that model changing environments, estimate state, reason under uncertainty, plan, control, adapt, and evaluate outcomes. Apply optimization, control theory, probabilistic reasoning, and reinforcement learning to create algorithms, prototypes, benchmarks, and product architectures. Collaborate with research, engineering, and product teams to transfer mathematical methodologies into real-world intelligent systems.

Architect and improve enterprise SaaS platform availability, resilience, scalability, performance, and cost efficiency. Define measurable production-readiness, capacity, recovery, and performance requirements; design failover and recovery mechanisms; eliminate bottlenecks and single points of failure; lead testing and remediation; optimize AI model usage and infrastructure economics; and drive architecture execution across Engineering, Cloud Infrastructure, DevOps, QA, and AI teams.

Lead client-facing strategic delivery for enterprise AI initiatives. Responsibilities include understanding customer priorities, facilitating workshops, defining AI use cases, developing deployment roadmaps, evaluating value and feasibility, tracking KPIs, aligning stakeholders, coordinating technical execution, supporting adoption and change management, and translating customer feedback into product improvements.

Lead enterprise AI transformation strategy and value realization across finance, tax, risk, governance, and compliance functions. Conduct discovery, define deployment roadmaps, prioritize use cases, establish KPIs, engage executive stakeholders, align initiatives with control frameworks, and guide deployment engineers during rollout. Translate complex regulated workflows into responsible, scalable AI implementations and contribute insights to product and engineering teams. Travel may be required for customer engagements and workshops.

Lead the security architecture and implementation for an enterprise AI platform: secure cloud infrastructure, data protection, IAM, tenant isolation, and GenAI workflows. Embed security into CI/CD, IaC, containers, secrets management, and operations. Design secure customer deployment patterns, support enterprise security reviews, and partner on compliance (SOC 2, ISO 27001, SSPA) while personally implementing critical security capabilities and guiding cross-functional engineering teams.

Lead product strategy and execution for AI-powered enterprise products. Drive development from zero to one and establish product processes.

The Senior GenAI Engineer will design, develop, and deploy scalable GenAI applications using Python and cloud technologies, primarily on Azure. Responsibilities include leading GenAI development, optimizing large language models, and mentoring junior developers.

Develop and test AI-powered software solutions, collaborating with team members and learning about AI tools and frameworks.

The Forward Deployed Engineer will work with enterprise customers to deploy and integrate the AI platform, troubleshoot issues, and enhance operational processes, ensuring successful and scalable implementations.

As a Senior Python Developer, you'll lead the design and deployment of scalable applications, enhance system performance, mentor junior developers, and drive innovation in software development.

Owner of hands-on calibration, configuration, and integration of an enterprise Generative AI platform. Design compliant decision logic, run automated validation and evaluation, integrate customer components, rapidly prototype reusable templates, and contribute to governance and delivery methodology to ensure production-ready, regulatory-compliant deployments.

As an Applied Data Science Intern, you will work on data innovation projects, focusing on AI-driven solutions, machine learning pipelines, and enterprise integrations while receiving mentorship from experienced professionals.

The Senior Enterprise System Integrator will design and implement integrations between the platform and major enterprise systems. Responsibilities include building APIs, troubleshooting issues, and collaborating with multiple teams to ensure reliable performance of integrations.

The Senior GenAI Engineer will develop and deploy Generative AI applications using Python and Azure, lead projects, mentor juniors, and stay innovative in AI trends.