Senior AI Engineer / Data Scientist
The Incubate Team 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
Scopra se questa possibilità è adatta a lei leggendo tutte le informazioni riportate di seguito.
agents: 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
small, 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.
Experience: 4–5+ years in product development
Location / Work model: Rome (Italy) / Hybrid
Mandate Skills
-
- Education: Bachelor’s or Master’s degree in Computer Science, Statistics, or a related quantitative field
- ML architectures: Deep Learning, Generative AI / LLMs, predictive models
- Agentic 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
- Scripting & OS: Bash shell scripting on Linux/Unix
What you will do
-
- Own 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.
- Build and evaluate GenAI solutions using state‑of‑the‑art and proprietary models; prototype quickly, measure rigorously,
and harden what works into production‑quality services.
- Define and implement evaluation pipelines and datasets to measure agent correctness, safety, robustness, and cost (e.g., red‑teaming, prompt‑injection resistance, regression suites).
- Apply data science methods — experiment design, statistical analysis, predictive modelling — to understand agent behaviour and drive product decisions with evidence.
- Package and deploy services as containers on Kubernetes; contribute to CI/CD, monitoring, and operational tooling.
- Provide technical leadership: lead design reviews, set engineering standards for the team, and mentor less experienced engineers.
- Collaborate with product, security, and platform teams; document designs and share findings through demos and technical write‑ups. xysqume
- Track the agentic/LLM ecosystem and bring relevant techniques, tools, and research into the team’s roadmap.
What we are looking for:
-
- BE/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
- Extensive experience in AI/ML technologies and professional software development practices (testing, code review, version control, CI/CD)
- Expert‑level Python
- Experience developing cutting‑edge GenAI solutions, exploring advanced AI models, and working with proprietary generative models
- Hands‑on experience with agent frameworks (LangChain, LangGraph, LlamaIndex) and vector/retrieval stacks
- Deep knowledge of Kubernetes and container‑based deployment; Docker proficiency
- Bash shell scripting in Linux/Unix environments
- Solid understanding of secure‑by‑design principles for AI systems: least privilege, sandboxing, input/output validation, secrets handling, auditability
- Ability to take ambiguous problems from concept to working solution with limited supervision
- Excellent written and verbal communication skills; able to explain complex technical trade‑offs to mixed audiences
Nice to have
-
- Java (or another JVM language) for integration with existing services
- Experience with MLOps/LLMOps tooling (model serving, experiment tracking, evaluation frameworks)
- Familiarity with agent interoperability protocols (e.g., MCP, A2A) and tool/plugin ecosystems
- Exposure to cloud platforms (AWS, Azure, GCP) and infrastructure‑as‑code
- Knowledge of AI governance, safety, or responsible‑AI frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLM Applications)
- Contributions to open‑source AI projects or publications
Candidate Data Privacy Notice
J-18808-Ljbffr
Scopra se questa possibilità è adatta a lei leggendo tutte le informazioni riportate di seguito.
agents: 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
small, 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.
Experience: 4–5+ years in product development
Location / Work model: Rome (Italy) / Hybrid
Mandate Skills
-
- Education: Bachelor’s or Master’s degree in Computer Science, Statistics, or a related quantitative field
- ML architectures: Deep Learning, Generative AI / LLMs, predictive models
- Agentic 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
- Scripting & OS: Bash shell scripting on Linux/Unix
What you will do
-
- Own 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.
- Build and evaluate GenAI solutions using state‑of‑the‑art and proprietary models; prototype quickly, measure rigorously,
and harden what works into production‑quality services.
- Define and implement evaluation pipelines and datasets to measure agent correctness, safety, robustness, and cost (e.g., red‑teaming, prompt‑injection resistance, regression suites).
- Apply data science methods — experiment design, statistical analysis, predictive modelling — to understand agent behaviour and drive product decisions with evidence.
- Package and deploy services as containers on Kubernetes; contribute to CI/CD, monitoring, and operational tooling.
- Provide technical leadership: lead design reviews, set engineering standards for the team, and mentor less experienced engineers.
- Collaborate with product, security, and platform teams; document designs and share findings through demos and technical write‑ups. xysqume
- Track the agentic/LLM ecosystem and bring relevant techniques, tools, and research into the team’s roadmap.
What we are looking for:
-
- BE/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
- Extensive experience in AI/ML technologies and professional software development practices (testing, code review, version control, CI/CD)
- Expert‑level Python
- Experience developing cutting‑edge GenAI solutions, exploring advanced AI models, and working with proprietary generative models
- Hands‑on experience with agent frameworks (LangChain, LangGraph, LlamaIndex) and vector/retrieval stacks
- Deep knowledge of Kubernetes and container‑based deployment; Docker proficiency
- Bash shell scripting in Linux/Unix environments
- Solid understanding of secure‑by‑design principles for AI systems: least privilege, sandboxing, input/output validation, secrets handling, auditability
- Ability to take ambiguous problems from concept to working solution with limited supervision
- Excellent written and verbal communication skills; able to explain complex technical trade‑offs to mixed audiences
Nice to have
-
- Java (or another JVM language) for integration with existing services
- Experience with MLOps/LLMOps tooling (model serving, experiment tracking, evaluation frameworks)
- Familiarity with agent interoperability protocols (e.g., MCP, A2A) and tool/plugin ecosystems
- Exposure to cloud platforms (AWS, Azure, GCP) and infrastructure‑as‑code
- Knowledge of AI governance, safety, or responsible‑AI frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLM Applications)
- Contributions to open‑source AI projects or publications
Candidate Data Privacy Notice
J-18808-Ljbffr