• Advance AI Capabilities: Contribute to early-stage development of initial-phase AI systems for retrieval, ranking, categorization, and generative AI features on unstructured healthcare data. • Outcome-Driven Engineering: Deliver outcomes through software and models, ensuring projects are aligned with business objectives. • AI System Design: Work backwards from extremely complex business problems to collaborate on the design of AI components and abstractions that solve intricate problems in a maintainable and logical way. • Bring Rigor to Science Decisions: Be accountable for scientific decisions to deliver outcomes. • Mentorship and Best Practices: Provide mentorship and promote best practices in data and AI while cultivating a collaborative and inclusive team culture. • Team Growth: Contribute to hiring efforts and help identify top AI talent.

Role Description 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, develop, and deploy tailored AI and automation solutions aligned to client objectives. - 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. - Build and deploy agentic workflows using LangChain, LangGraph, and Google ADK, including tool calling and structured outputs. - Implement RAG pipelines using vector databases and search technologies (e.g., Pinecone, Elasticsearch, pgvector) and graph databases when appropriate. - Ship fast prototypes, then harden them into scalable systems (testing, reliability, deployment, monitoring) independently or with a team. - Participate in discovery, run technical calls/demos when needed, and communicate tradeoffs clearly to client and internal stakeholders. - Improve deployed solutions through feature work, bug fixes, monitoring, prompt/model improvements, and additional automations. - Produce clear technical documentation, client demos, and internal playbooks to enable reuse and scalability. - Stay current on LLM tooling and delivery best practices to improve quality and speed. Success in This Role Looks Like - 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. Qualifications - 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. Preferred Qualifications - 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. Company Description 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. 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.

• Build scalable, reliable AI systems that serve millions of users • Create sophisticated AI agents that can chain multiple LLM calls • Develop and optimize prompting strategies • Build robust APIs and integrate AI capabilities with existing infrastructure • Implement comprehensive evaluation frameworks, A/B testing, and monitoring systems • Optimize for cost, latency, and scalability • Collaborate closely with product teams, backend engineers, and stakeholders

• The Automation and AI Engineer is a member of the HR Technology team and responsible for designing, building, and implementing intelligent plugins and automations that enhance our enterprise workflows. • In this role, you will be responsible for executing the HR Automation and AI product roadmap, aligning with HR organization goals and helping maximize value from investments in Agentic AI platforms. • Learn the ins-and-outs of the Moveworks/Serval and other AI platforms with hands-on training. • Design, build, and deploy custom plugins for Moveworks or Serval platforms. • Collaborate closely with key stakeholders across COEs, HR and IT to gather and validate requirements. • Lead successful implementation of the AI agents & integrations by working closely with your team as well as key stakeholders. • Conduct technical analysis and workshops with IT and Solution Architects to design the integrations for these AI agents.