Role Description You will be instrumental in building an enterprise-grade Agentic Framework designed to automate complex production lifecycles in heavy industries like aerospace and manufacturing. This role is for engineers who thrive in "high-unknown" environments and can move from architecture design to production-grade implementation with minimal oversight. We value candidates who leverage heavy AI code assistance to accelerate delivery without sacrificing quality. Qualifications - Technical Excellence: - Python Mastery: Expert-level Python with experience in FastAPI or Django. - Agentic Frameworks: Proven experience with LangGraph and LangChain for building complex LLM pipelines. - Production-Grade Engineering: Deep understanding of TDD, linters, and ensuring high test coverage within CI/CD processes. - Data Proficiency: Experience in data processing using tools like Pandas, Polars, or PySpark. - API Development: Ability to build scalable backends for real-time interactions, including WebSockets. - Soft Skills & Mindset: - Extreme Ownership: Ability to take a project from business requirements through to implementation with minimal oversight. - Analytical Decision-Making: Ability to justify architectural choices (e.g., Vector DB vs. Corporate Standard) using data-driven Proof of Concepts. - High Velocity: Comfortable working in a small, fast-paced team where rapid delivery is essential. - Defined Execution: Strong commitment to establishing a clear "Definition of Done" for every task. - Nice to have skills: - Familiarity with .NET to align with existing corporate standards and facilitate cross-team integration. - Cloud-Native AI Guardrails: Experience with Azure Content Safety or AWS Bedrock Guardrails to manage model compliance. - Advanced Monitoring: Experience with Arize Phoenix or similar tools for debugging agent behavior and tool-calling efficiency. - LLM Fine-Tuning/Prompting: Knowledge of few-shot learning and structured prompting using XML, JSON, or Markdown. - Enterprise Databases: Familiarity with Azure Cosmos DB, MongoDB, or MSSQL. Responsibilities - Core Platform Development: Responsible for the development of the core agent platform components. - Architecture Transition: Lead the transition from a JavaScript prototype to a segregated Python application with dedicated Management APIs and Agent Execution Backends. - Agent Orchestration: Build and manage complex agent workflows using LangChain and LangGraph to handle multi-step interactions and tool calls. - No-Code & Pro-Code Tooling: Develop a platform that allows administrators to build agents via a "no-code" approach while enabling advanced users to execute custom code scripts. - RAG & Search Optimization: Implement advanced retrieval strategies, including hybrid search and rerankers, while justifying database choices through proof-of-concept metrics. - Guardrails & Compliance: Ensure AI safety by implementing logits masking, context managers, and content guardrails to prevent harmful or off-brand generation. Benefits - Udemy courses of your choice. - Team-buildings, events, marathons & charity activities to connect and recharge. - Workshops, trainings, expert knowledge-sharing that keep you growing. - Clear career path. - Absence days for work-life balance. - Flexible hours & work setup - work from any of listed locations and organize your day your way.

Intellectsoft is a software development company delivering innovative solutions since 2007. We operate across North America, Latin America, the Nordic region, the UK, and Europe. We specialize in industries like Fintech, Healthcare, EdTech, Construction, Hospitality, and more, partnering with startups, mid-sized businesses, and Fortune 500 companies to drive innovation and scalability. Our clients include Jaguar Motors, Universal Pictures, Harley-Davidson, and many more where our teams are making daily impact. Together, our team delivers solutions that make a difference. Learn more at https://www.intellectsoft.net/ You will be instrumental in building an enterprise-grade Agentic Framework designed to automate complex production lifecycles in heavy industries like aerospace and manufacturing. This role is for engineers who thrive in "high-unknown" environments and can move from architecture design to production-grade implementation with minimal oversight. We value candidates who leverage heavy AI code assistance to accelerate delivery without sacrificing quality

• Contribute to the design, implementation, and maintenance of the LLM Gateway, focusing on features like unified API endpoints for internal/externally hosted LLM, rate/token limit management, and intelligent failover mechanisms to boost uptime and reliability. • Designed and developed ML and Generative AI systems in cloud-based production environments at scale. • Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines. • Implement and operationalize agentic AI workflows with tool use using frameworks such as LangChain and LangGraph. • Drive adoption of MLOps / LLMOps practices, including CI/CD automation, versioning, testing, and lifecycle management. • Establish best practices for observability, monitoring, evaluation, and governance of GenAI pipelines in production. • Strong ownership mindset and platform thinking. • Ability to lead AI platform delivery from concept to production.

• Spend the majority of your time embedded with engineering teams — working alongside developers to stand up AI coding tools, establish platform standards, and introduce agentic workflows into their daily delivery process • Run hands-on enablement programs including workshops, lunch-and-learns, one-on-one coaching sessions, and team-level onboarding for AI development tools like Claude Code and GitHub Copilot • Assess team-level AI readiness across the 350+ person technology organization — identifying gaps in documentation, coding standards, context files, and workflow design that limit AI effectiveness • Prescribe and deliver targeted interventions that help teams get more value from the agentic platform — from CLAUDE.md setup and prompt engineering to agentic workflow configuration and best practice adoption • Collect structured feedback from every team you serve and translate it into actionable requirements and adoption signal for the platform team’s backlog • Produce playbooks, best practice guides, and enablement materials that help engineering teams integrate AI tools into their existing Git, Jira, Confluence, and GitHub Actions workflows without disrupting delivery • Define and track adoption indicators across teams — measuring what’s working, what’s stuck, and what needs adjustment — and present clear signal to platform leadership on enablement progress • Serve as the connective tissue between the platform team’s capabilities and the engineering organization that depends on them — equal parts technologist, coach, and internal consultant