Senior AI Systems Engineer

We are seeking a highly experienced Senior AI Systems Engineer with a strong background in full-stack software engineering and demonstrated experience designing, building, and deploying intelligent or semi-intelligent software systems.

This role is suited to an accomplished software engineer who has evolved beyond traditional application development and now applies large language models, AI platforms, data systems, automation tools, and modern software engineering practices to solve complex business and technical problems.

The successful candidate will be comfortable working across the complete technology stack—from user interfaces, APIs, integrations, and cloud infrastructure to data pipelines, AI agents, retrieval systems, model orchestration, and evaluation frameworks. They will have hands-on experience working with tools and platforms within the Claude and ChatGPT ecosystems, as well as other commercial and open-source AI technologies.

This is not a purely research-focused or prompt-engineering position. The role requires someone who can turn ambiguous ideas and business requirements into reliable, scalable, secure, and maintainable software products.

Key Responsibilities

AI Systems and Intelligent Application Development

• Design, build, and deploy production-ready applications that incorporate large language models, machine learning capabilities, intelligent workflows, or semi-autonomous agents.

• Develop AI-powered software using platforms and tools from the Claude, ChatGPT, and broader generative AI ecosystems.

• Build systems that combine LLMs with traditional software components, business rules, APIs, databases, data platforms, and human approval workflows.

• Design and implement agentic systems capable of reasoning, planning, retrieving information, invoking tools, and completing multi-step tasks.

• Develop retrieval-augmented generation solutions using vector databases, semantic search, document processing, knowledge graphs, and structured data sources.

• Build orchestration layers that route requests between models, tools, data sources, workflows, and software services.

• Create evaluation, monitoring, logging, and feedback mechanisms to measure the accuracy, reliability, cost, latency, and usefulness of AI systems.

• Identify situations where deterministic software, machine learning, LLM-based reasoning, or a hybrid approach is most appropriate.

Full-Stack Software Engineering

• Architect and develop scalable web applications, APIs, backend services, integrations, and user-facing interfaces.

• Translate prototypes and proofs of concept into secure, maintainable, production-quality software.

• Design modular software architectures that support ongoing changes in models, providers, data sources, and business requirements.

• Develop and maintain integrations with third-party applications, SaaS platforms, enterprise systems, and cloud services.

• Apply strong software engineering practices, including automated testing, version control, code review, continuous integration, continuous deployment, observability, and technical documentation.

• Diagnose and resolve complex technical issues across application, data, infrastructure, and AI layers.

• Contribute directly to implementation while also providing architecture and technical leadership.

Data and Platform Engineering

• Design data pipelines and processing workflows that prepare structured and unstructured information for use by AI applications.

• Work with relational databases, document stores, vector databases, APIs, event streams, and enterprise data platforms.

• Implement document ingestion, chunking, classification, enrichment, indexing, retrieval, and data-quality processes.

• Establish appropriate data governance, access controls, privacy protections, and auditability for AI-powered systems.

• Build systems that can reason across operational data, business documents, historical interactions, and external information sources.

• Collaborate with data engineers, analysts, and business stakeholders to ensure data is accurate, accessible, and fit for purpose.

Architecture and Technical Leadership

• Lead the technical design of intelligent software platforms from initial discovery through production deployment.

• Evaluate AI models, software frameworks, platforms, and vendors based on performance, security, cost, scalability, and business value.

• Define architecture standards and reusable patterns for AI-enabled application development.

• Identify technical risks and recommend practical mitigations, particularly around hallucinations, data leakage, prompt injection, model limitations, security, and operational reliability.

• Mentor engineers and provide guidance on software architecture, AI development practices, system design, and implementation quality.

• Contribute to technical roadmaps, estimates, solution designs, and delivery plans.

• Remain current with developments across generative AI, agentic systems, model-context protocols, AI-assisted development, and enterprise AI platforms.

Client and Stakeholder Engagement

• Work directly with clients, product leaders, business stakeholders, and technical teams to understand problems and identify valuable AI use cases.

• Translate business objectives into clear technical requirements, architectures, prototypes, and delivery plans.

• Explain complex AI and software concepts in clear, practical language to both technical and non-technical audiences.

• Lead technical workshops, discovery sessions, solution demonstrations, and architecture reviews.

• Communicate the capabilities, limitations, risks, assumptions, and tradeoffs associated with proposed AI solutions.

• Build confidence with stakeholders through strong judgment, structured thinking, transparency, and dependable delivery.

Required Qualifications

• At least 7 years of professional software engineering experience, including substantial experience as a senior, lead, principal, or architect-level full-stack engineer.

• Demonstrated experience designing and building production software across frontend, backend, API, database, integration, and cloud infrastructure layers.

• Hands-on experience building applications using large language models and generative AI platforms.

• Experience with tools, APIs, or development environments within the OpenAI/ChatGPT and Anthropic/Claude ecosystems.

• Demonstrated experience building intelligent or semi-intelligent applications, AI agents, decision-support tools, automation platforms, conversational systems, or AI-enabled data products.

• Strong proficiency in at least one modern backend programming language, such as Python, TypeScript, JavaScript, Java, C#, Go, or a comparable language.

• Experience with modern frontend technologies such as React, Next.js, Angular, Vue, or similar frameworks.

• Strong understanding of API design, distributed systems, asynchronous processing, authentication, authorization, and enterprise integration patterns.

• Experience working with relational databases and at least one non-relational, document, search, graph, or vector database technology.

• Practical knowledge of prompt design, context management, tool use, structured outputs, function calling, embeddings, semantic search, and retrieval-augmented generation.

• Experience deploying and operating applications within AWS, Microsoft Azure, Google Cloud Platform, or a comparable cloud environment.

• Strong understanding of software development lifecycle practices, automated testing, source control, code review, CI/CD, logging, monitoring, and production support.

• Excellent written and verbal communication skills.

• Demonstrated ability to communicate effectively with engineers, executives, clients, product teams, and non-technical stakeholders.

• Strong analytical thinking, technical judgment, problem-solving ability, and attention to detail.

• Ability to operate effectively in environments where requirements may initially be ambiguous or evolving.

Preferred Qualifications

• Experience building multi-agent systems or applications that use tools, memory, planning, delegation, and human-in-the-loop workflows.

• Experience with frameworks or platforms such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or comparable orchestration technologies.

• Familiarity with Model Context Protocol, tool servers, AI gateways, model routing, and standardized AI integration patterns.

• Experience with vector databases and search platforms such as Pinecone, Weaviate, Milvus, pgvector, Elasticsearch, OpenSearch, Azure AI Search, or similar technologies.

• Experience designing model evaluation frameworks, automated test suites, red-team scenarios, guardrails, and observability for AI systems.

• Understanding of model selection, token usage, inference costs, caching, latency optimization, and provider abstraction.

• Experience with fine-tuning, model adaptation, synthetic data, traditional machine learning, or self-hosted open-source models.

• Familiarity with data engineering tools, event-driven architectures, workflow engines, and enterprise data platforms.

• Experience integrating AI solutions with CRM, ERP, service management, collaboration, analytics, or content-management platforms.

• Experience delivering software or AI solutions in a consulting, professional services, product development, or client-facing environment.

• Experience leading small engineering teams or mentoring senior and mid-level developers.

• Familiarity with AI security, privacy, governance, compliance, intellectual-property, and responsible-AI considerations.

Technical Areas of Interest

• Large language models and multimodal models

• AI agents and agentic workflows

• Claude, Claude Code, Anthropic APIs, and related development tools

• ChatGPT, OpenAI APIs, Codex, Assistants, Responses, and related development tools

• Retrieval-augmented generation

• Prompt and context engineering

• Tool calling and structured model outputs

• Model Context Protocol and tool integrations

• Vector search and semantic retrieval

• Knowledge graphs and enterprise search

• AI evaluation, monitoring, and observability

• Document intelligence and unstructured data processing

• Workflow automation and human-in-the-loop systems

• Cloud-native application development

• Full-stack web and application architecture

• Data platforms, analytics systems, and enterprise integrations

Success in This Role

• Convert business challenges into practical, valuable, and reliable AI-enabled software solutions.

• Deliver working systems rather than isolated demonstrations or experimental prototypes.

• Balance speed of innovation with sound architecture, security, maintainability, and operational control.

• Select appropriate technologies without becoming overly dependent on a single AI model, provider, or framework.

• Clearly communicate technical decisions, limitations, risks, and expected outcomes.

• Build trust with clients and internal stakeholders.

• Raise the technical capability of the broader engineering team.

• Produce software and AI systems that create measurable improvements in productivity, decision-making, customer experience, or operational performance.

Ideal Candidate Profile

You are an experienced software engineer who understands that successful AI products require much more than connecting an application to a language-model API.

You know how to combine AI capabilities with robust software architecture, reliable data, thoughtful user experiences, security controls, evaluation methods, and operational workflows. You are equally comfortable discussing system architecture with senior engineers, demonstrating a prototype to business leaders, and writing production code to solve the hardest parts of the problem.

You are curious and experimental, but also pragmatic. You understand both the transformative potential and the current limitations of AI systems. You can move quickly without sacrificing engineering discipline, and you communicate clearly enough to bring technical and non-technical stakeholders with you.