Role Description We are looking for a Senior Engineer to lead development of Generative AI and agentic frameworks supporting enterprise-scale AI transformation. - Architect and implement GenAI and agent-based solutions - Develop scalable AI pipelines integrated with cloud platforms - Ensure governance, reliability, and performance of AI systems - Collaborate across teams to deliver enterprise AI capabilities Qualifications - Strong experience in GenAI, AI/ML, and LLM technologies - Cloud platform expertise (AWS, Azure, GCP) - Proven hands-on development experience Requirements - Experience with Python, PyTorch, LangChain, and MLOps - Data Engineering exposure: Databricks, Snowflake, Synapse, BigQuery - Alignment with cloud/data platform architecture strategies - Knowledge of Responsible AI, governance, and compliance Company Description

• 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.