• The Forward Deployed AI Engineer will work directly with clients and senior engineers to build AI-powered systems inside real enterprise environments. • Understand operational workflows and convert them into technical artifacts. • Connect to real enterprise systems and data sources. • Build context-aware AI agents, retrieval systems, and automation prototypes. • Help move the best prototypes toward reliable production deployments. • Help ingest, clean, structure, and connect data from enterprise systems into context graph. • Build AI pipelines and agent-based systems that can reason over enterprise context, identify patterns, surface workflow gaps, and suggest or trigger automations. • Integrate with client infrastructure such as databases, APIs, cloud storage, document repositories, analytics tools, ticketing systems, and internal applications. • Rapidly build proof-of-concepts that show how AI can improve a client's operations. • Work with senior engineers and business stakeholders to translate ambiguous operational problems into technical designs, data models, prompts, pipelines, and deployed applications. • Help build reliable, maintainable systems that can run in production environments, including client cloud environments when needed.

Role Description We're building a network of AI Engineers who can design, build, and integrate practical AI features into existing products. We're looking for people to collaborate with on a freelance basis - part-time or full-time, depending on project needs. This role is focused on delivery, not ML research - you don't need to be a data scientist. What matters most is your engineering foundation and your ability to use AI to solve real business problems. Qualifications - Strong software engineering background (Node.js or Ruby preferred) - Experience integrating LLM APIs into production systems - Ability to design pragmatic AI solutions within existing architectures - Understanding of cost, latency and reliability constraints of AI systems - Ability to explain AI limitations to clients and guide realistic expectations - Solid communication skills and a consultative mindset - English B2/C1 - Availability part-time or full-time (B2B contract) - Familiarity with modern AI tooling: LangChain, LlamaIndex, or Vercel AI SDK Requirements - Adding AI functionality into existing Node.js / Ruby / Python / React / React Native codebases - Building LLM-powered features: chat, summaries, classification, smart search, document Q&A - Designing lightweight RAG pipelines using embeddings and vector search - Working with vector DBs (pgvector, Pinecone, Qdrant) - Implementing safe, reliable LLM endpoints (OpenAI, Anthropic, Azure) - Working with PMs and clients to shape realistic AI features and transform workflows to reduce manual effort - Advising clients when NOT to use AI and considering trade-offs related to latency, accuracy, cost and maintainability Benefits - Flexible working hours - Opportunity to work on diverse projects - Collaborative and innovative environment Company Description Read more at Monterail Tech Network

• Drive the technical architecture across the domain, with a focus on modernization, scalability and AI integration. • Lead the design and implementation of microservices and cloud-native systems. • Guide the transition from legacy systems to modern distributed systems. • Collaborate with senior stakeholders (EMs, Staff and Principal Engineers, Directors) to align on technology direction. • Champion engineering excellence, fostering a culture of autonomy, accountability, and quality. • Provide mentorship and leadership across engineering teams. • Integrate LLMs and other GenAI models into web applications through efficient API design and implementation. • Build and optimize API endpoints enabling seamless, real-time communication between front-end applications and back-end AI services. • Design and develop secure, scalable, and high-performing Java-based microservices for AI model deployment. • Develop robust back-end systems in Java to support deployment, scalability, and ongoing maintenance of GenAI models. • Build and maintain data pipelines, including preprocessing input data and post-processing model outputs for application use. • Implement best practices for sensitive data handling and maintaining high model performance. • Use Kubernetes and Docker for containerization and orchestration to ensure scalable deployment of AI applications. • Implement CI/CD pipelines for automated testing and delivery of code changes. • Maintain scalable and secure cloud infrastructure using platforms such as Google Cloud Platform or Azure for model training, storage, and deployment. • Utilize vector databases (e.g., Pinecone, Weaviate, Faiss) for embedding management and similarity search. • Work with frameworks supporting model development and deployment, including Hugging Face, LangChain, and OpenAI ecosystem tools. • Optimize and fine-tune LLMs based on specific application needs.

Role Description The Forward Deployed AI Engineer will work directly with clients and senior engineers to build AI-powered systems inside real enterprise environments. This role is ideal for a strong junior engineer who wants to work on practical AI systems, enterprise data, agents, retrieval, workflow automation, and context graphs. You will help connect to client systems, process messy real-world data, build prototypes, and turn business problems into working software. The work is hands-on and client-facing: - Understand operational workflows and convert them into technical artifacts. - Connect to real enterprise systems and data sources. - Build context-aware AI agents, retrieval systems, and automation prototypes. - Help move the best prototypes toward reliable production deployments. Build context graphs. Help ingest, clean, structure, and connect data from enterprise systems into context graphs. This may include structured databases, PDFs, spreadsheets, tickets, CRM data, analytics events, Slack or Teams exports, meeting transcripts, operational workflows, and other internal knowledge sources. Develop AI and agentic workflows. Build AI pipelines and agent-based systems that can reason over enterprise context, identify patterns, surface workflow gaps, and suggest or trigger automations. This may involve LLMs, retrieval systems, structured extraction, tool use, LangGraph-style workflows, and agent harnesses. Connect to enterprise systems. Integrate with client infrastructure such as databases, APIs, cloud storage, document repositories, analytics tools, ticketing systems, and internal applications. Prototype automation opportunities. Rapidly build proof-of-concepts that show how AI can improve a client's operations - for example, process documentation, workflow discovery, incident management, document extraction, customer journey analysis, or operational decision support. Turn messy business problems into software. Work with senior engineers and business stakeholders to translate ambiguous operational problems into technical designs, data models, prompts, pipelines, and deployed applications. Support production deployments. Help build reliable, maintainable systems that can run in production environments, including client cloud environments when needed. Qualifications - Strong Python fundamentals. - Basic backend development experience. - Familiarity with APIs, databases, and cloud services. - Interest in LLMs, agents, retrieval, structured outputs, and tool-calling. - Familiarity with data pipelines and messy real-world datasets. - Experience with FastAPI, LangChain, LangGraph, vector databases, document processing, or knowledge graphs is a plus. - AWS experience is a plus. - Experience with enterprise data, analytics, or workflow automation is a strong plus. Requirements - You like figuring out how businesses actually operate. - You are comfortable working with incomplete, messy, or poorly documented data. - You can move quickly from vague requirements to a working prototype. - You care about building useful systems, not just impressive demos. - You communicate clearly with both engineers and non-technical stakeholders. - You are curious about how AI agents can interact with real tools, data, and workflows. - You want to learn how to deploy AI into real enterprise environments. Benefits - You will work on the frontier of practical enterprise AI: connecting AI agents to real company context and using that context to discover and automate high-value workflows. - This is a hands-on engineering role for someone who wants to grow into building production-grade AI systems across data, agents, infrastructure, and enterprise software.