Note: The job is a remote job and is open to candidates in USA. Sapience AI is hiring an Applied AI-Agent Engineer to build dependable applied AI and agentic systems for its MINERVA platform. The role focuses on agent design, grounding and retrieval, safe tool use, evaluation, and production reliability so community members can access and act on trusted knowledge.

Responsibilities

• Design and build the agents that let MINERVA understand requests, reason, and take action
• Own orchestration, planning, and tool use, and keep it dependable
• Design for the limits of models, not just their strengths
• Ground agent behavior in the KO graph and the COGENT architecture so answers rest on real community knowledge
• Build retrieval that surfaces the right knowledge at the right time
• Reduce confident errors through grounding and verification
• Integrate neural and symbolic reasoning into applied behavior in partnership with neuro-symbolic AI
• Decide where a model should reason and where structure should carry the load
• Turn reasoning advances into product behavior
• Build the tools and actions agents use, including safe access to systems and data
• Make actions reliable, observable, and reversible where they should be
• Connect applied AI to the systems a community already runs
• Build the guardrails that keep agent behavior safe and within bounds
• Handle uncertainty honestly, and make provenance visible
• Treat member trust as a design constraint
• Build evaluation for agent quality, including accuracy, groundedness, and safety
• Instrument applied systems so you can tell whether they are working in production
• Iterate on real behavior, not just offline tests
• Take applied AI from prototype to dependable production, in partnership with ML infrastructure
• Balance capability against latency, cost, and reliability
• Own the behavior members experience, not just the demo

Skills

• Five or more years in software engineering, with strong recent work in applied AI or ML
• Hands-on experience building agentic systems, LLM applications, or retrieval-augmented systems in production
• Strong understanding of grounding, tool use, and the limits of models
• Experience making AI behavior safe, reliable, and observable
• Strong Python, plus solid software engineering fundamentals
• Rigor about evaluation and honest handling of uncertainty
• Care for member trust and safety
• Agent design, orchestration, planning, and tool use
• Grounding, retrieval, and reducing confident errors
• Integrating neural and symbolic reasoning into applied behavior
• Guardrails, safety, and provenance
• Evaluation and instrumentation of AI behavior
• Productionizing AI features reliably
• Software engineering fundamentals for AI systems
• LLM application and agent frameworks, plus orchestration tooling
• Retrieval frameworks, vector databases, and graph access
• Python as the primary language, plus TypeScript where needed
• Evaluation and experiment-tracking tooling
• Observability for AI behavior in production
• Cloud platforms and containers
• Building applied behavior on the MINERVA platform, KO graph, and COGENT architecture
• Prior applied AI, ML engineering, or agent engineering at a software or AI company
• A track record of shipping AI features that people actually rely on
• Experience making AI behavior safe and dependable in production
• Experience with agent frameworks, orchestration, and planning
• Familiarity with knowledge graphs and neuro-symbolic reasoning
• Experience with retrieval, embeddings, and hybrid search
• Experience productionizing AI features at scale
• Domain understanding of professional or knowledge-intensive communities
• Experience with grounding and retrieval systems is a plus

Benefits

• Early stage equity
• Generous health and wellness benefits

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