Sr. Engineer Data Science & Agentic AI

Title

Sr. Engineer Data Science & Agentic AI

As a Data Science & Agentic AI Sr. Engineer, you will develop, design, implement, and deliver advanced data science, machine learning, and agentic AI products for machine maintenance. You will create predictive and prescriptive maintenance solutions, including AI agents and multi‑agent workflows that retrieve knowledge, reason over data, use approved tools and APIs, and recommend or automate maintenance actions with appropriate human oversight.

• Perform data wrangling, exploratory, descriptive, predictive, prescriptive, and agent‑enabled analysis and visualization on both recurring and ad hoc basis to support the Projects Manager and the Maintenance user base.
• Identify new opportunities for intelligent process automation, Agentic AI, KPIs, visualizations, reports, dashboards, and decision‑support products aligned with leadership’s strategic objectives.
• Lead the entire data science, machine learning, and agentic AI lifecycle: problem definition, data collection, model/agent design, deployment, monitoring, governance, and continuous improvement.
• Ensure seamless integration and coordination across data pipelines, ML/DL models, LLM applications, agentic workflows, APIs, and business processes, optimizing safety, scalability, efficiency, and business impact.
• Establish robust monitoring mechanisms for deployed models and AI agents, enabling proactive identification of performance, reliability, drift, safety, cost, and governance issues.
• Define and execute overall machine learning, deep learning, and Agentic AI strategy aligned with business goals, maintenance reliability priorities, and enterprise technology standards.
• Design, build, and deploy AI agents and multi‑agent workflows using LLMs, RAG, vector search, tool/function calling, workflow orchestration, and secure API integrations.
• Integrate agentic workflows with CMMS/EAM, maintenance, asset, IoT, historian, PLC/SCADA, cloud, and enterprise data platforms while maintaining human‑in‑the‑loop controls for higher‑risk actions.
• Establish Agent Ops, LLMOps, and MLOps practices for prompt/version management, agent evaluation, observability, guardrails, traceability, cost monitoring, model drift detection, and continuous improvement.
• Implement agentic AI safety, privacy, and security controls, including least‑privilege access, data protection, prompt‑injection mitigation, approval gates, audit trails, and responsible AI governance.
• Drive large‑scale data science, ML/DL, and Agentic AI projects leveraging data transformation, machine learning models, LLM applications, and intelligent workflow automation.
• Develop predictive maintenance tools, AI agents, and insights for customers while balancing data complexity, coding/visualization platforms, reliability requirements, risk controls, and client demands.
• Automate and streamline projects, reports, maintenance workflows, and agent‑enabled decision processes to increase efficiency, scalability, and adoption.
• Document project requirements, methodologies, architecture decisions, agent workflows, evaluation results, risks, and outcomes.
• Prepare technical reports, presentations, and user guides to communicate AI/ML/Agentic AI solutions to stakeholders.
• Stay updated with the latest advancements in AI/ML, Agentic AI, LLMs, RAG, vector search, orchestration frameworks, and industrial automation; conduct research and experiments to improve models and agents.
• Ensure compliance with ethical standards, legal requirements, privacy, bias, explainability, autonomy, human oversight, and potential misuse of AI/ML models or AI agents.
• Share expertise in AI/ML, Agentic AI, responsible automation, and insights with colleagues, stakeholders, and team members; conduct training sessions or workshops.
• Actively collaborate with project management teams, IT professionals, reliability engineers, maintenance leaders, and business stakeholders to identify opportunities for AI agents, ML models, and automation.

• 5–7 years experience in Python, R, or another programming language.
• 5–7 years experience with Tensor Flow, PyTorch, scikit‑learn, or comparable ML…