AI Engineer – Agentic AI, Cloud Discovery

Pragmatike

. Design and build LLM-powered analysis and classification pipelines, then productionize them as Go services .

Posted 9/17/2026full-timeRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building LLM-powered analysis and classification pipelines, with strong proficiency in Go and Python for production services and experimentation. Capable of integrating AI capabilities with security systems while ensuring model quality and performance through continuous evaluation and monitoring.

Highest-signal resume keywords

Go ProgrammingPython ProgrammingLLM API ExperienceCloud-Native DeploymentAI-Assisted Development Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringMachine LearningPrompt EngineeringStructured ExtractionModel EvaluationRegression SuitesDockerKubernetesAWSGCP

Soft Skills

Strong Communication SkillsOwnershipIndependent Work

Tools & Technologies

Claude CodeCursorGitHub CopilotCodexVector Databases

Industry Keywords

Security AnalyticsSIEMSOARDigital ForensicsCybersecurity

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecurityDockerGoogle Cloud PlatformKubernetesPythonGo

About the role

Key responsibilities & impact

Design and build LLM-powered analysis and classification pipelines, then productionize them as Go services
Prototype approaches in Python, including prompting strategies, RAG, structured extraction, and ML classifiers
Ship solutions that meet defined accuracy targets
Define ground-truth datasets, evaluation metrics, and regression suites to continuously measure and improve model quality
Monitor model quality and drift in production and build processes to identify and address degradation
Collaborate with security researchers to translate attack patterns and risk signals into detection and summarization logic
Integrate AI-powered capabilities with event, storage, and UI layers to surface actionable results to security teams
Use AI-assisted development workflows to accelerate implementation, testing, debugging, and experimentation
Participate in interviews discussing real-world AI use in software development, validation of AI output, and changes to working practices

Requirements

What you’ll need

4+ years of software engineering experience, including 2+ years shipping LLM- or ML-backed features to production
Strong Go skills for production backend services
Strong Python skills for experimentation and data pipelines
Hands-on experience with LLM APIs, prompt engineering, structured outputs, and RAG
Experience evaluating LLM/ML systems through offline evaluations, human review, regression suites, or similar approaches
Understanding of AI agent architectures, including tool calling, MCP or similar protocols, multi-step planning, and common failure modes
Experience with cloud-native deployment using Docker, Kubernetes, and AWS, GCP, or Azure
Fluent English with strong written and verbal communication skills
Comfortable using modern AI coding assistants such as Claude Code, Cursor, GitHub Copilot, Codex, or similar; this is a must-have
Strong ownership and ability to work independently in a remote-first, distributed environment
Nice-to-have: background in security analytics, SIEM/SOAR, or digital forensics
Nice-to-have: knowledge of major cloud provider APIs, IAM models, and resource inventory
Nice-to-have: experience with vector databases, embedding pipelines, or model fine-tuning
Nice-to-have: familiarity with tracing AI applications and OpenTelemetry-style observability
Nice-to-have: previous experience in cybersecurity, security tooling, or trust & safety
Nice-to-have: experience introducing AI-assisted or agentic development workflows across engineering teams

Benefits

Comp & perks

Work on a greenfield product at the intersection of cybersecurity and agentic AI
Turn cutting-edge LLM/ML approaches into production systems protecting enterprise customers
Work across AI experimentation and production engineering, from Python prototypes to Go services
Take ownership of a key workstream and influence architecture from an early stage
Collaborate with a highly technical, distributed team where AI is a core part of the development process
Remote-first work arrangement