Senior AI Engineer

Responsibilities

Design and build AI capabilities for the Edwin AI platform, including incident summarization, root-cause analysis, and guided remediation workflows. Collaborate with cross-functional teams to ensure AI systems are accurate, reliable, and secure while optimizing performance and cost.

Requirements

Requires a bachelor's degree in a technical field and 5-8+ years of experience in software or machine learning engineering. Candidates must have strong Python skills and hands-on experience with LLM application development, RAG pipelines, and AI orchestration frameworks.

Skills

• Python
• LLM
• RAG
• Machine Learning
• LangChain
• LlamaIndex
• Vector Databases
• Prompt Engineering
• Observability
• AIOps
• Distributed Systems
• Kubernetes
• API Design
• Data Engineering
• Software Engineering
• Cloud Computing

Benefits

• Great Place To Work certified
• Performance and recognition culture
• Professional development

About this role

About Us:
We love going to work and think you should too. Our team is dedicated to trust, customer obsession, agility, and striving to be better everyday. These values serve as the foundation of our culture, guiding our actions and driving us towards excellence. We foster a culture of performance and recognition, allowing us to transform growth as we enable our employees to do the best work of their careers.
This position is located in Pune. You'll be working in a major tech center of Pune, India. Across the globe, our Centers of Energy serve as hubs where we accelerate productivity and collaboration, inspire creativity, and cultivate a culture of connection and celebration. Our teams coordinate their time in Centers of Energy to reflect how they work best.
To learn more about life at LogicMonitor, check out our Careers Page.
What You'll Do:
LogicMonitor® is the AI-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise.
Our customers love LogicMonitor's ability to bring cloud and traditional IT together into one view, as seen in minimal churn rates, expansion business, and exciting new customer references. In fact, LogicMonitor has received the highest Net Promoter Score of any IT Infrastructure Management provider. LogicMonitor also boasts high employee satisfaction. We have been certified as a Great Place To Work®, and named one of BuiltIn's Best Places to Work for the seventh year in a row!
LogicMonitor is redefining observability through Edwin AI, our GenAI-powered AIOps platform that helps engineering and operations teams understand, investigate, and resolve issues faster across complex hybrid environments. Edwin AI brings together telemetry, topology, alerts, metrics, logs, traces, incidents, and operational context with the reasoning capabilities of large language models.
We’re looking for a Senior AI Engineer to design, build, evaluate, and operate the AI systems that power Edwin’s intelligence. This role sits at the intersection of applied machine learning, LLM application engineering, retrieval-augmented generation, data intelligence, and production software engineering.
You will build AI workflows that transform complex observability signals into clear summaries, correlations, root-cause hypotheses, anomaly explanations, recommendations, and guided remediation steps. You’ll work closely with AI Platform, Backend, Data, AIOps, SRE, Product, and Design teams to make Edwin accurate, reliable, explainable, secure, and impactful for customers.
Here's a closer look at this key role:
Design and build AI capabilities for Edwin AI, including incident summarization, alert correlation, anomaly explanation, root-cause analysis assistance, recommendation generation, and guided remediation workflows.
Develop production-grade LLM applications using techniques such as retrieval-augmented generation, tool calling, function calling, prompt orchestration, agentic workflows, structured outputs, and multi-step reasoning pipelines.
Build and optimize RAG pipelines that retrieve relevant observability context from metrics, logs, traces, alerts, topology, incidents, runbooks, knowledge bases, and historical operational data.
Design embedding, indexing, chunking, reranking, query expansion, and semantic retrieval strategies for high-scale observability and AIOps use cases.
Develop AI orchestration services using Python, FastAPI, LangGraph, LangChain, LlamaIndex, or similar frameworks.
Implement structured AI workflows that combine deterministic business logic, statistical methods, retrieval systems, and LLM reasoning.
Collaborate with backend and data engineering teams to define AI-ready data contracts, metadata schemas, context formats, and retrieval APIs.
Build evaluation frameworks for AI quality, including relevance, groundedness, correctness, hallucination reduction, latency, cost, safety, and user usefulness.
Create offline and online evaluation pipelines using golden datasets, synthetic test cases, human review, A/B testing, feedback loops, and production telemetry.
Optimize prompt templates, model selection, retrieval strategies, context windows, latency, token usage, cost, and response consistency.
Implement guardrails for safe and trustworthy AI behavior, including source attribution, citation handling, confidence signals, output validation, policy checks, and fallback strategies.
Work with structured and unstructured observability data to power AI reasoning across events, incidents, alerts, logs, metrics, topology, and service dependencies.
Integrate with commercial and open-source LLMs, embedding models, rerankers, vector databases, and model-serving infrastructure.
Build APIs and services that expose AI capabilities to Edwin’s UI, backend systems, and AIOps workflows.
Instrument AI pipelines with observability, tracing, metrics, structured logs, evaluation signals, and debugging tools.
Partner with SRE and platform teams to monitor production AI behavior, troubleshoot failures, and improve reliability across end-to-end AI workflows.
Collaborate with Product and Design to shape AI UX patterns such as citations, reasoning summaries, confidence indicators, feedback capture, explainability, and human-in-the-loop interactions.
Ensure model inputs, retrieved context, generated outputs, and stored AI artifacts follow data privacy, RBAC, tenant isolation, and compliance requirements.
Participate in technical design discussions, architecture reviews, code reviews, and AI best-practice development.
Mentor engineers on LLM application patterns, RAG design, evaluation methods, and prod...

About LogicMonitor

Software Development · 1,001-5,000 employees · headquartered in Santa Barbara, CA · founded 2007