AI Engineer – Agentic AI & Cloud Discovery at Pragmatike in India

Job description

• Application
• Location: India, Remote-First
• Employment: Full-Time
• Start Date: November 2026
• Experience: 4+ years
• Language: Fluent English required
• Industry: Cybersecurity / Enterprise SaaS / AI Security
• Pragmatike is recruiting on behalf of a global enterprise cybersecurity company building a new generation of products to secure AI agents, LLM-powered applications, and the data they access.
• The engineering organization is scaling rapidly in India, with two AI Engineer openings focused on turning AI and machine learning capabilities into reliable, production-grade security products.
• We’re looking for engineers who are equally comfortable building production services in Go and developing LLM/ML pipelines in Python, with the ability to move quickly from experimentation to hardened production systems.
• Agentic AI Forensics: Turn agent traces, prompts, tool calls, and security events into structured, explainable findings for security analysts using LLM reasoning, RAG, structured extraction, and rigorous evaluation.
• Cloud Discovery: Classify cloud resources and workloads to identify AI agents, model endpoints, and AI-enabled applications, while inferring their purpose and security risk using LLM/ML classification at scale.
• Both workstreams share the same foundations: Go for production services, Python for experimentation and data pipelines, LLM APIs, rigorous evaluation, and cloud-native deployment.

Responsibilities

• 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, and 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.

Requirements

• 4+ years of software engineering experience, including 2+ years shipping LLM- or ML-backed features to production.
• Strong Go skills for production backend services and 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 the ability to work independently in a remote-first, distributed environment.
• Background in security analytics, SIEM/SOAR, or digital forensics.
• Practical knowledge of major cloud provider APIs, IAM models, and resource inventory.
• Experience with vector databases, embedding pipelines, or model fine-tuning for classification or extraction.
• Familiarity with tracing AI applications and OpenTelemetry-style observability.
• Previous experience in cybersecurity, security tooling, or trust & safety.
• Experience introducing AI-assisted or agentic development workflows across engineering teams.

Skills

Required

• Go
• Python
• LLM APIs
• Prompt engineering
• Structured outputs
• RAG
• Evaluation of LLM/ML systems
• AI agent architectures
• Tool calling
• MCP or similar protocols
• Multi-step planning
• Docker

Preferred

• Security analytics
• SIEM/SOAR
• Digital forensics
• Cloud provider APIs
• IAM models
• Resource inventory
• Vector databases
• Embedding pipelines

Benefits

• 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 both 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.