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Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
Type: Full-Time, Remote
As an Applied AI Engineer(Automation), you will deliver high-impact AI and automation solutions for clients—owning work from requirements discovery through prototype and production deployment. You’ll build reliable, maintainable systems that integrate LLMs into real business workflows via APIs, automation platforms, and backend services.
This is a mid-to-senior individual contributor role. You’ll collaborate closely with Solutions Architects, Delivery/Engagement leads, and Product Managers to scope, build, ship, and iterate on client solutions.
Key Responsibilities- Design & Deploy: Design, develop, and deploy tailored AI and automation solutions aligned to client objectives.
- Build Workflows & Services: Translate business problems into production-grade AI workflows and services using Python, automation tools (n8n/Make/Zapier or similar), and LLM platforms/APIs (e.g., OpenAI, IBM watsonx.ai, Amazon Bedrock), plus retrieval systems.
- Agentic Systems: Build and deploy agentic workflows using LangChain, LangGraph, and Google ADK, including tool calling and structured outputs.
- Retrieval & Knowledge Systems: Implement RAG pipelines using vector databases and search technologies (e.g., Pinecone, Elasticsearch, pgvector) and graph databases when appropriate.
- Prototype → Production: Ship fast prototypes, then harden them into scalable systems (testing, reliability, deployment, monitoring) independently or with a team.
- Client Partnership: Participate in discovery, run technical calls/demos when needed, and communicate tradeoffs clearly to client and internal stakeholders.
- Ongoing Support & Iteration: Improve deployed solutions through feature work, bug fixes, monitoring, prompt/model improvements, and additional automations.
- Documentation: Produce clear technical documentation, client demos, and internal playbooks to enable reuse and scalability.
- Continuous Learning: Stay current on LLM tooling and delivery best practices to improve quality and speed.
- Solutions consistently meet or exceed client expectations and show measurable impact (time saved, cost reduced, improved conversion/deflection, faster cycle time).
- Clients trust you as a go-to engineering partner and expand usage of deployed AI workflows.
- Deliveries are production-ready: monitored, testable, documented, and maintainable.
- 3–8 years of software or AI engineering experience (mid-to-senior).
- 2–3+ years of AI Automation, Generative AI, or Agentic AI (mid-to-senior).
- Strong Python engineering skills and experience building APIs/services (e.g., FastAPI).
- Hands-on experience integrating LLMs (e.g., OpenAI APIs or equivalents), including prompt design, structured outputs, and basic evaluation practices.
- Experience with at least one workflow automation platform (n8n, Make, Zapier, or similar) and building reliable integrations.
- Familiarity with RAG fundamentals and retrieval systems (embeddings, vector search); exposure to vector databases and/or Elasticsearch.
- Production engineering fundamentals: Docker, cloud deployment (AWS/GCP/Azure/IBM), and experience with async/queuing patterns (e.g., Celery, Redis, Kafka).
- Comfort operating in a client-facing environment: technical calls, demos, and collaborating with cross-functional stakeholders.
- Experience with fine-tuning LLMs or other ML models; broader ML exposure is a plus (not required).
- Familiarity with observability and tracing (e.g., LangSmith, OpenTelemetry) and prompt/version lifecycle management.
- Experience with graph databases / knowledge graphs.
- Familiarity with data governance and AI governance concepts (PII handling, auditability, access controls, risk awareness).
- Prior consulting experience or work in fast-paced startup environments.
