Senior AI Software Engineer (Agentic AI)

Dotmatics•Boston, MA

2d•$145,000 - $196,000•Remote

About The Position

We are seeking a Senior AI Engineer (Full stack) with advanced Python skills to join our team building and advancing the Luma platform—an AI-Native Multimodal Research Platform for Scientific Intelligence. A platform that helps customers analyze and configure data within their system. This role offers the opportunity to work on cutting-edge AI/ML technology, architecting scalable microservices, and delivering production-ready systems that integrate with enterprise data platforms. You will build AI agents that perform real work—systems that plan tasks, call tools, execute workflows, interact with APIs, and integrate with real-world data sources. You'll be working in a collaborative, agentic coding environment where modern development tools support your workflow, enabling rapid iteration while maintaining high code quality standards.

Requirements

10+ years of professional software development experience, including significant experience owning and delivering large-scale technical systems.
Ability to design durable architectures, independently lead high-impact engineering efforts, and mentor other engineers while maintaining exceptional coding standards.
Enjoy architecting clean, resilient services, solving complex systems problems, and shaping long-term technical direction for the areas you support.
Expert-level Python 3.11+ with deep understanding of async/await, type hints, and modern Python best practices.
Experience building AI agents using LangChain/LangGraph, including tool creation, step planning, function calling, retrieval workflows, and reliable agent-state management.
FastAPI experience building production RESTful APIs, streaming endpoints (SSE), and async request handling.
Strong PostgreSQL expertise (including performance tuning and schema design) and SQLAlchemy.
Strong understanding of dependency injection, clean architecture, and functional programming concepts.
Experience designing and scaling microservices in production environments.
Ability to assess engineering risk, propose rollout strategies, and make high-impact architectural decisions.
Experience building safe execution environments and guardrails for AI decision-making.
React & TypeScript with modern hooks and state management patterns (Redux/Context).
Experience with Webpack Module Federation and micro-frontend architectures.
Ability to design responsive, maintainable UI components using SCSS/CSS.
Familiarity with Jest for robust frontend testing practices.
LangChain & LangGraph expertise for building AI agent workflows, tool orchestration, and LLM integration
Proven experience building LLM-powered applications with frameworks like LangChain, LangGraph, or similar
Understanding of Retrieval-Augmented Generation (RAG) patterns and vector embeddings
Experience with agent orchestration, tool creation, and multi-step reasoning workflows
Familiarity with LLM serving endpoints (Databricks, OpenAI, Anthropic, or similar)
Knowledge of streaming responses, callback systems, and real-time feedback mechanisms
Understanding of Model Context Protocol (MCP) for tool integration

Nice To Haves

Production Kubernetes experience with Helm charts and orchestration
Experience with Databricks or similar cloud data platforms
LangChain/LangGraph production implementations

Responsibilities

Design and implement AI agent workflows and tooling using LangChain/LangGraph, enabling AI models to plan actions, call tools, use APIs, search information, and reliably complete multi-step workflows.
Build and maintain the tools, function interfaces, and system connectors that AI agents use to interact with databases, document stores, enterprise apps, and external APIs.
Ensure AI agents operate safely, follow rules, respect permissions, and reliably execute within defined constraints.
Lead and execute the design and implementation of core workflow orchestration and tooling features, including automated tasks and background processes.
Build scalable FastAPI services with well-defined RESTful APIs and real-time streaming endpoints.
Create modular, reusable service components with strong authentication, error handling, and pagination patterns.
Develop and guide React frontend components for real-time interactions and data visualization.
Implement multi-tenant architecture with secure isolation, resource boundaries, and long-term scalability.
Provide technical guidance to other engineers during implementation, ensuring high-quality, maintainable solutions.
Evaluate risk when implementing new features or refactoring, and propose safe rollout strategies.
Clean Architecture principles with clear separation of concerns
Microservices design patterns including service discovery, API gateways, and interservice communication
Event-driven architecture with message queues and async processing
Subprocess isolation patterns for credential management and security boundaries
Influence architectural direction across teams, helping bring clarity and structure to ambiguous problems.
Architect robust AI agent execution layers that ensure determinism, observability, and reliable stepwise execution.
Write comprehensive automated tests using pytest and Jest, including integration and behavior-driven tests.
Implement structured logging, correlation IDs, and observability patterns to ensure system clarity and operability.
Contribute to and improve CI/CD pipelines with automated testing, linting, and deployment workflows.
Set up effective monitoring and alerting for production systems.
Lead or support critical incident resolution with calm, context-driven decision-making.
Drive platform-wide improvements in performance, reliability, and technical quality.
Exercise independent judgment in methods, techniques, and evaluation criteria to ensure robust outcomes.
Instrument AI agent systems with monitoring, tracing, and guardrails to ensure safe and predictable behavior in production.
Document architectural decisions, engineering patterns, and approaches that become long-term references for the team.
Provide approach summaries and technical proposals before major implementations to ensure alignment with product and engineering partners.
Participate in planning and estimation, applying deep technical judgment and strong product awareness.
Mentor engineers, raise team capabilities, and guide others through complex engineering workflows (feature branches, PRs, ticket management).
Build relationships across engineering and product groups, influencing roadmaps and cross-team initiatives.
Communicate risks, challenges, and opportunities proactively and clearly to stakeholders.
Document and evangelize best practices for safe, reliable, and maintainable AI agent design.