AI Threat Intel Developer

Description:

Responsibilities:

- Design and develop an AI-driven Threat Intel Platform focused on fraud, risk, and anomaly detection.

- Build and extend agentic frameworks using LangChain, LangGraph, or similar architectures for reasoning, orchestration, and task automation.

- Design, develop, and maintain backend services and microservices using Python, Go, and cloud-native technologies.

- Build secure, scalable, and high-performance systems leveraging AWS services, including RDS, DynamoDB, Lambda, EKS/Kubernetes, and event-driven pipelines.

- Develop and manage MLOps pipelines for scalable model training, testing, deployment, and real-time inference.

- Implement end-to-end automation workflows, from data ingestion and feature engineering to autonomous inference and actioning.

- Integrate LLMs and autonomous AI agents for dynamic pattern recognition, event correlation, and decision automation.

- Integrate software components into robust, production-ready systems with clear documentation, testing, and maintainability.

- Apply test-driven development and CI/CD practices using Git, Jenkins, Terraform, Docker, and cloud-native tooling.

- Collaborate with data science, threat intelligence, and platform engineering teams to deliver intelligent, context- aware solutions.

- Use advanced debugging and analytical techniques to solve complex distributed-systems and AI workflow challenges.

- Mentor junior developers and contribute to team growth through knowledge sharing and technical leadership.

- Participate in R& D demos, innovation initiatives, and annual hackathons to explore and showcase new ideas.

Requirements :

- 3+ years of experience in software engineering, machine learning, or backend/data systems development.

- Strong focus on backend engineering with experience building large-scale, distributed, or real-time systems.

- Hands-on experience with fraud/risk analytics, behavioral modeling, or anomaly detection (fintech, ad tech, cybersecurity domains preferred).

- Proven experience building or integrating agentic AI frameworks (LangChain, LangGraph, Semantic Kernel, etc. ).

- Strong programming skills in Python (preferred) or Go, and familiarity with PyTorch, TensorFlow, or Scikit-learn.

- Experience with cloud-native architectures and tools: Kubernetes, Docker, Terraform, and event-driven systems.

- Practical knowledge of MLOps, data pipeline design, feature stores, and real-time inference systems.

- Understanding of AI automation, LLM-based orchestration, prompt engineering, and evaluation strategies.

- Familiarity with cloud platforms (preferably AWS) and distributed systems at scale.

- Proven ability to work independently on large features, drive technical discussions, and deliver production-quality systems.

- Strong collaboration and communication skills, with a passion for mentoring and team development.

- A proactive mindset and the ability to adapt existing approaches to solve new challenges.