Sr. Prompt Engineer

LogicMonitor; Pune District; On-site; INR 180,000 - 320,000; Full time

Job summary

LogicMonitor in Pune/Bangalore is seeking a hands-on prompt engineer to help deliver dependable enterprise AI solutions. You will work on the AI Fabric foundation, building governed workflows, and integrating prompts, models, data sources, and APIs to support go-to-market initiatives.

You will apply established patterns for prompt and context management, automate with Python-based tooling, and collaborate with stakeholders and technical owners from use-case definition through launch and ongoing

Qualifications

• Hands-on experience building enterprise AI solutions.
• Strong knowledge of prompts, system-instruction, and context management.
• Experience integrating data sources and APIs into AI workflows.

Responsibilities

• Build and deliver enterprise AI solutions on the AI Fabric using established patterns.
• Apply prompt and context management best practices to ensure grounded outputs.
• Develop automation workflows and lightweight code when needed to support durable runtime behavior.

Skills

Prompt engineering

Python

Workflow automation

LLM / AI tooling

API integration

Tools

Strands SDK

n8n

APIs

Job description

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/ Bangalore. 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!

This is a hands‑on individual‑contributor role for a prompt engineer who helps deliver dependable enterprise AI solutions for defined go‑to‑market and business problems. You will build on the AI Fabric - the shared foundation for governed execution, trusted context, reusable integrations, and observability - and use automation plus code to deliver workflows people can rely on.

You will work across prompts, models, context, data, tool and API integrations, workflow automation, evaluations, and operational controls. This is not a prompt‑only role: you will apply established platform patterns to build and improve well‑scoped solutions, partnering with stakeholders and the appropriate technical owners from use‑case definition through launch, measurement, and continuous improvement.

Here's a closer look at this key role:

• Partner with stakeholders to clarify defined go‑to‑market and business use cases, then build and deliver enterprise AI solutions on the shared AI Fabric using established patterns, approved connections, and agreed scope. Contribute improvements rather than creating one‑off or parallel implementations.
• Apply and iteratively improve established prompt, system‑instruction, structured‑output, tool‑use, model‑routing, and context‑window practices so outputs are useful, grounded, predictable, and fit for a business workflow.
• Build and support n8n or comparable automation workflows using established patterns for triggers, APIs, webhooks, human approvals, retries, fallbacks, scheduled jobs, and error handling. Contribute Python, Strands SDK, or other lightweight code when a workflow needs approved custom logic or durable runtime behavior.
• Integrate AI solutions with enterprise systems and governed data sources such as Slack, Salesforce, Gong, Jira, Confluence, Google Workspace, Snowflake, AWS services, and internal APIs. Pull, join, and validate data from the appropriate tables while applying established access, data-handling, and audit standards.
• Apply established context and data standards to select the approved sources for a use case; shape, prioritize, and refresh context; manage context‑window limits; support permission‑aware retrieval; and keep response behavior grounded in source data. Escalate new source, access, or architecture decisions to the appropriate owners.
• Apply established decision criteria for LLM-driven and deterministic components. For example, use models for language understanding and generation while relying on SQL, rules, validated APIs, and calculation services for financial, policy, or data‑accuracy‑critical operations; escal… new architectural tradeoffs when needed.
• Apply existing evaluation and quality controls, including representative test sets, regression tests, LLM-as-judge or deterministic scoring, failure taxonomies, prompt and retrieval experiments, and release criteria. Contribute evidence and improvements to the shared evaluation approach.
• Use approved observability and operational practices for production solutions, including structured logs, outcome and quality metrics, token and cost visibility, audit trails, alerting, troubleshooting