About the role
We are looking for a Senior Software Engineer (Full Stack + GenAI) who thrives on innovation, automation, and cutting-edge technology. This role will be central to designing, developing, testing, and scaling our enterprise SaaS security orchestration and automation applications - now supercharged with GenAI and intelligent automation.
Responsibilities:
- Design and develop large-scale subsystems for our next-gen AI - driven security automation platform.
- Integrate GenAI capabilities (LLMs, embeddings, vector databases) into orchestration workflows for faster, smarter security incident triage.
- Create intuitive APIs and AI-first integrations for seamless interaction with third-party ecosystems.
- Build and optimize systems leveraging GenAI for natural language interfaces, auto-remediation, and intelligent summarization.
- Translate proof-of-concepts in AI/ML and GenAI into highly functional, production-grade systems.
- Drive best practices in full-stack development, GenAI model integration, MLOps, and test automation.
- Mentor teams in modern software engineering and responsible AI development practices.
- Collaborate globally with cross-functional teams to deliver against tight deadlines with quality and innovation.
Requirements:
- Tech Stack: C#, . NET Core, Python, NodeJS, TypeScript, Angular, MongoDB, Postgres, REST APIs, Microservices, AWS, Kubernetes, Terraform, LangChain, OpenAI/Anthropic APIs, Hugging Face models, Vector DBs (Pinecone/Weaviate/Milvus), RAG pipelines, AI model fine-tuning, MLOps
- BE / BTech / ME / MTech in Computer Science, AI/ML, or a related field (or equivalent work experience).
- 8-12 years of progressive experience in software development, designing distributed systems at scale.
- Expertise in algorithms, data structures, design patterns, and AI/ML integration into production systems.
- Strong programming skills in C#/. NET Core, NodeJS, TypeScript, Angular, and Python.
- Hands-on experience with NoSQL/SQL databases (MongoDB, Postgres, MySQL) and vector databases.
- Experience in GenAI/LLM applications- prompt engineering, RAG pipelines, fine-tuning, and API integrations.
- Proficiency with real-time applications (WebSockets, SignalR).
- Cloud expertise: AWS services, Kubernetes, Terraform, and AI/ML cloud services (AWS Sagemaker, Bedrock, or equivalent).
- Strong focus on MLOps pipelines and observability for GenAI-based features.
- Excellent communication skills to work across diverse teams.
Nice to have (bonus):
- Experience with security products or cybersecurity automation.
- Knowledge of LLMOps, ethical AI, and AI governance.
- Contributions to open-source AI/GenAI projects.