Specialist, AI Engineer

Merck Merck · Pharma · Telangana, India

AI Engineer role focused on designing, building, and deploying production-grade agentic AI systems for cyber defense and enterprise security optimization. The role involves developing LLM-based agents, decision engines, and multi-step reasoning systems integrated into security platforms, with a strong emphasis on safe deployment in regulated environments.

What you'd actually do

  1. Build LLM-based agents capable of contextual reasoning, summarization, classification, and workflow execution.
  2. Engineer prompt strategies and structured evaluation frameworks to ensure reliability and repeatability.
  3. Design guardrails, kill switches, and rollback mechanisms to ensure safe AI deployment in regulated environments.
  4. Integrate AI outputs into enforcement platforms (e.g., conditional access triggers, device isolation logic, adaptive workflows).
  5. Build model monitoring and feedback loops to continuously improve precision and reduce false positives.

Skills

Required

  • Python
  • modern ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, or equivalent)
  • Deploying production AI systems integrated into enterprise platforms or APIs
  • LLM-based systems, prompt engineering, or agent-based workflows
  • Supervised and unsupervised learning techniques
  • Building evaluation metrics for model performance and business impact
  • Translating ambiguous operational problems into structured AI solutions
  • Systems-thinking mindset and engineering discipline

Nice to have

  • Microsoft Defender XDR, Sentinel, KQL, or related security platforms
  • Microsoft Copilot Studio or similar enterprise AI orchestration tools
  • MITRE ATT&CK mapping, risk engines, or behavioral threat modeling
  • Integrating AI outputs into automation platforms (ServiceNow, Logic Apps, API-driven workflows)
  • Implementing AI governance, drift monitoring, and production lifecycle management
  • Graph-based reasoning or ontology-driven AI models
  • Building multi-agent orchestration systems

What the JD emphasized

  • production-grade AI systems
  • agentic AI
  • LLM-based agents
  • contextual reasoning
  • workflow execution
  • prompt strategies
  • structured evaluation frameworks
  • multi-agent systems
  • safe autonomous enforcement
  • regulated environments
  • production-ready datasets
  • model monitoring
  • feedback loops
  • ontology-driven reasoning
  • entity-aware AI decisioning
  • deploy production AI systems
  • enterprise platforms or APIs
  • agent-based workflows
  • evaluation metrics
  • ambiguous operational problems
  • structured AI solutions
  • systems-thinking mindset
  • engineering discipline
  • Microsoft Defender XDR
  • Sentinel
  • KQL
  • Microsoft Copilot Studio
  • enterprise AI orchestration tools
  • MITRE ATT&CK mapping
  • risk engines
  • behavioral threat modeling
  • AI governance
  • drift monitoring
  • production lifecycle management
  • graph-based reasoning
  • ontology-driven AI models
  • AI-driven agent or model
  • live enterprise workflow
  • measurable reduction in manual triage
  • operational steps
  • ticket volume
  • monitoring and evaluation frameworks
  • model performance
  • AI outputs
  • conditional automation actions
  • critical AI decisioning logic
  • autonomous security workflows
  • platform-level enforcement
  • measurable efficiency gains
  • explainable AI outputs
  • governance and executive review
  • core builder of autonomous defense
  • operational optimization capabilities

Other signals

  • Develops agentic AI systems
  • Builds LLM-based agents
  • Engineers prompt strategies and evaluation frameworks
  • Designs guardrails and kill switches for safe AI deployment