Responsibilities:

• Design, develop, and deploy AI/ML solutions for event correlation, log analysis, root cause prediction, and observability enhancement.
• Build and optimize LLM-powered systems, including RAG pipelines, MoE models, and vector-search architectures.
• Implement multi-model orchestration layers or MCP frameworks to manage diverse AI components and workflows.
• Collaborate with engineering, DevOps, and SRE teams to integrate AIOps capabilities into operational environments.
• Develop scalable APIs and backend services using Python and FastAPI.
• Leverage statistics, ML algorithms, and analytical techniques to generate actionable operational insights.
• Work with MLOps and AIOps toolchains to automate model deployment, monitoring, and maintenance.
• Evaluate system performance, drive continuous improvement initiatives, and deliver quantifiable operational benefits.
• Prepare documentation, architecture diagrams, and best practices for AI implementations.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field.
• 5+ years of experience in AI/ML, Data Engineering, or AIOps domains.
• Demonstrated success implementing LLM-based solutions for log/event correlation, incident prediction, or operational intelligence.
• Strong understanding of AIOps concepts including event correlation, anomaly detection, incident prediction, and topology mapping.
• Hands-on experience with RAG workflows, MoE models, and vector databases (FAISS, Pinecone, Milvus).
• Proficient in Python, FastAPI, and AI integration frameworks such as LangChain, LlamaIndex, or Transformers.
• Experience building MCP or multi-model orchestration layers.
• Solid grounding in data science, statistics, and core ML algorithms.
• Familiarity with AIOps platforms like Moogsoft, BigPanda, Dynatrace, or IBM Watson AIOps.
• Proven impact with measurable outcomes (e.g., reduced alert noise, faster incident resolution).