Senior AI Engineer / Data Scientist
pThe bIncubate Team /b explores and implements cutting‑edge applications in Agentic AI and Machine Learning. The team is currently building a platform for the safe execution of AI /p pagents: an environment where autonomous agents can run, call tools, and act on data with isolation, policy enforcement, observability, and human oversight built in. You will join a /p psmall, hands‑on team that moves from research idea to working prototype to production‑grade component, and you will be expected to own significant parts of that journey. /p pbExperience: 4–5+ years in product development /b /p pbLocation / Work model: Rome (Italy) / Hybrid /b /p pbMandate Skills /b /p ul liEducation: Bachelor’s or Master’s degree in Computer Science, Statistics, or a related quantitative field /li liML architectures: Deep Learning, Generative AI / LLMs, predictive models /li liAgentic AI: proven experience designing and shipping LLM-based agents (tool/function calling, multi‑agent orchestration, RAG, memory) integrated with external systems via APIs or protocols such as MCP /li liScripting OS: Bash shell scripting on Linux/Unix /li /ul pbWhat you will do /b /p ul liOwn the design and delivery of core components of the agent execution platform: agent runtime, tool/function-calling layer, sandboxing and isolation, policy and guardrail enforcement, and audit/observability. /li liBuild and evaluate GenAI solutions using state‑of‑the‑art and proprietary models; prototype quickly, measure rigorously, and harden what works into production‑quality services.
/li liDefine and implement evaluation pipelines and datasets to measure agent correctness, safety, robustness, and cost (e.g., red‑teaming, prompt‑injection resistance, regression suites). /li liApply data science methods — experiment design, statistical analysis, predictive modelling — to understand agent behaviour and drive product decisions with evidence. /li liPackage and deploy services as containers on Kubernetes; contribute to CI/CD, monitoring, and operational tooling. /li liProvide technical leadership: lead design reviews, set engineering standards for the team, and mentor less experienced engineers. /li liCollaborate with product, security, and platform teams; document designs and share findings through demos and technical write‑ups. /li liTrack the agentic/LLM ecosystem and bring relevant techniques, tools, and research into the team’s roadmap. /li /ul pbWhat we are looking for: /b /p ul liBE/BTech or above in Computer Science, Statistics, or related stream; 4–5+ years of product development experience,
with a track record of shipping AI/ML features to production /li liExtensive experience in AI/ML technologies and professional software development practices (testing, code review, version control, CI/CD) /li liExpert‑level Python /li liExperience developing cutting‑edge GenAI solutions, exploring advanced AI models, and working with proprietary generative models /li liHands‑on experience with agent frameworks (LangChain, LangGraph, LlamaIndex) and vector/retrieval stacks /li liDeep knowledge of Kubernetes and container‑based deployment; Docker proficiency /li liBash shell scripting in Linux/Unix environments /li liSolid understanding of secure‑by‑design principles for AI systems: least privilege, sandboxing, input/output validation, secrets handling, auditability /li liAbility to take ambiguous problems from concept to working solution with limited supervision /li liExcellent written and verbal communication skills; able to explain complex technical trade‑offs to mixed audiences /li /ul pbNice to have /b /p ul liJava (or another JVM language) for integration with existing services /li liExperience with MLOps/LLMOps tooling (model serving, experiment tracking, evaluation frameworks) /li liFamiliarity with agent interoperability protocols (e.g., MCP, A2A) and tool/plugin ecosystems /li liExposure to cloud platforms (AWS, Azure, GCP) and infrastructure‑as‑code /li liKnowledge of AI governance, safety, or responsible‑AI frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLM Applications) /li liContributions to open‑source AI projects or publications /li /ul pbCandidate Data Privacy Notice /b /p #J-18808-Ljbffr
/li liDefine and implement evaluation pipelines and datasets to measure agent correctness, safety, robustness, and cost (e.g., red‑teaming, prompt‑injection resistance, regression suites). /li liApply data science methods — experiment design, statistical analysis, predictive modelling — to understand agent behaviour and drive product decisions with evidence. /li liPackage and deploy services as containers on Kubernetes; contribute to CI/CD, monitoring, and operational tooling. /li liProvide technical leadership: lead design reviews, set engineering standards for the team, and mentor less experienced engineers. /li liCollaborate with product, security, and platform teams; document designs and share findings through demos and technical write‑ups. /li liTrack the agentic/LLM ecosystem and bring relevant techniques, tools, and research into the team’s roadmap. /li /ul pbWhat we are looking for: /b /p ul liBE/BTech or above in Computer Science, Statistics, or related stream; 4–5+ years of product development experience,
with a track record of shipping AI/ML features to production /li liExtensive experience in AI/ML technologies and professional software development practices (testing, code review, version control, CI/CD) /li liExpert‑level Python /li liExperience developing cutting‑edge GenAI solutions, exploring advanced AI models, and working with proprietary generative models /li liHands‑on experience with agent frameworks (LangChain, LangGraph, LlamaIndex) and vector/retrieval stacks /li liDeep knowledge of Kubernetes and container‑based deployment; Docker proficiency /li liBash shell scripting in Linux/Unix environments /li liSolid understanding of secure‑by‑design principles for AI systems: least privilege, sandboxing, input/output validation, secrets handling, auditability /li liAbility to take ambiguous problems from concept to working solution with limited supervision /li liExcellent written and verbal communication skills; able to explain complex technical trade‑offs to mixed audiences /li /ul pbNice to have /b /p ul liJava (or another JVM language) for integration with existing services /li liExperience with MLOps/LLMOps tooling (model serving, experiment tracking, evaluation frameworks) /li liFamiliarity with agent interoperability protocols (e.g., MCP, A2A) and tool/plugin ecosystems /li liExposure to cloud platforms (AWS, Azure, GCP) and infrastructure‑as‑code /li liKnowledge of AI governance, safety, or responsible‑AI frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLM Applications) /li liContributions to open‑source AI projects or publications /li /ul pbCandidate Data Privacy Notice /b /p #J-18808-Ljbffr