Artificial Intelligence Engineer

We are seeking a highly skilled AI / LLM Engineer to design, build, and own production-grade AI systems that power enterprise operations. This role involves developing intelligent agent workflows, drift detection systems, alert correlation engines, Retrieval-Augmented Generation (RAG) pipelines, automated documentation agents, and ChatOps solutions using modern LLM technologies.

The ideal candidate has hands-on experience building and deploying AI applications in production environments, with strong expertise in LangGraph, Python, Pydantic, RAG architectures, vector databases, and agent orchestration frameworks. This is an engineering-focused role requiring a strong emphasis on reliability, auditability, scalability, and measurable business outcomes.

Key Responsibilities

• Design and develop LangGraph-based agent workflows and state machines with conditional routing, state persistence, and human-in-the-loop checkpoints.
• Build intelligent drift detection agents for configuration, usage, compliance, and cost monitoring using structured and explainable outputs.
• Develop alert correlation and event intelligence capabilities, including graph traversal, parent-child suppression, deduplication, alert clustering, and confidence scoring.
• Design and implement enterprise-scale RAG pipelines, including chunking strategies, embedding selection, metadata filtering, retrieval optimization, and vector database design.
• Build AI-powered documentation agents that generate structured technical documentation from source code repositories and engineering artifacts.
• Develop and maintain ChatOps agents integrated with enterprise systems and knowledge repositories.
• Implement structured output schemas using Pydantic and ensure deterministic, auditable AI responses.
• Create evaluation frameworks, benchmark datasets, automated tests, and quality metrics for AI system performance.
• Collaborate with backend, platform, cloud, and data engineering teams on API contracts and solution architecture.
• Produce technical documentation covering design decisions, confidence scoring methodologies, thresholds, assumptions, and known limitations.
• Participate in architecture reviews, code reviews, and production deployment activities.

Must-Have Skills & Experience

• 3+ years of experience building and deploying production AI/ML systems.
• Hands-on experience with LangGraph, including workflow orchestration, state management, node/edge patterns, and conditional execution.
• Strong Python development expertise.
• Experience designing structured outputs using Pydantic or equivalent frameworks.
• Proven experience building production-grade LLM applications and agentic workflows.
• Strong understanding of prompt engineering, evaluation methodologies, and AI system testing.
• Experience building deterministic and auditable AI workflows.
• Hands-on experience with retrieval-augmented generation (RAG) systems.
• Experience with vector databases such as pgvector, Pinecone, Weaviate, Chroma, or similar platforms.
• Strong understanding of embeddings, chunking strategies, retrieval tuning, and metadata filtering.
• Experience writing unit tests, integration tests, and evaluation suites for AI solutions.

Good to Have

• LangSmith or similar LLM observability and tracing tools.
• Experience with graph-based algorithms and NetworkX.
• Alert correlation, incident management, or AIOps experience.
• Re-ranking techniques for improving retrieval relevance.
• Microsoft Teams Bot Framework development.
• Azure OpenAI, Azure AI Foundry, and Azure DevOps integrations.
• ServiceNow integrations and workflow automation.
• Knowledge of SRE practices, DORA metrics, FinOps, or cloud governance.

Key Skills

LangGraph | Generative AI | LLMs | RAG | pgvector | Python | Pydantic | Agentic AI | Prompt Engineering | Vector Databases | Azure OpenAI | LangSmith | NetworkX | ServiceNow | ChatOps

Why Join Us?

• Work on large-scale enterprise AI systems with real business impact.
• Build production-ready AI solutions beyond prototypes and proof of concepts.
• Own end-to-end design, development, testing, and deployment of modern AI platforms.
• Collaborate with high-performing engineering, cloud, and data teams.
• Opportunity to shape next-generation AI-driven operations and automation capabilities.