AI Engineer – Agentic AI & Cloud Discovery
Sign up free to see how well your resume matches this role.
What you'll do
- 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.
What they're looking for
- 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.
Nice to have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Location: India, Remote-First
Employment: Full-Time
Start Date: November 2026
Experience: 4+ years
Language: Fluent English required
Industry: Cybersecurity / Enterprise SaaS / AI Security
About the Opportunity
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.
You’ll join one of two workstreams:
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.
What You’ll Do
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.
What We’re Looking For
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.
Nice to Have
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.
AI-First Engineering
AI is a core part of the engineering workflow on this team.
During the interview process, you'll be asked about how you use AI in real-world software development, including the tools you use, how you validate their output, and where AI has changed the way you work.
We're looking for engineers who use AI as a force multiplier for quality, productivity, and problem-solving, not simply as an autocomplete tool.
Why Join
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.
Pragmatike is committed to a fair, transparent, and inclusive recruitment process. We do not discriminate based on age, disability, gender, gender identity or expression, marital or civil partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation.
In accordance with GDPR, your personal data will be processed lawfully, fairly, and securely and used solely for recruitment purposes, including sharing it with our client(s) for employment consideration.
Company
Company facts come from this company's own listings. We only show what the postings themselves carry.