AI Engineer – Agentic AI, Cloud Discovery

Job not on LinkedIn

🔥 11 minutes ago

🇮🇳 India – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Pragmatike

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

💼 Consulting

📣 Marketing

🎯 Recruiter

Consulting • Marketing • Recruitment

Pragmatike is a remote IT recruitment and staffing company that sources, vets, and places international tech talent for businesses. They provide human-curated matching (not solely AI), technical assessments, and fast placements—claiming qualified specialists can be introduced within 48 hours—while handling onboarding, invoicing and international payroll. Pragmatike serves startups and enterprises with roles like developers, data engineers, mobile and game developers, product managers and specialists, operating across 60+ countries with a large vetted talent pool.

📋 Description

• 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

• 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

• 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