About the Role

We are seeking a talented GenAI/AIOps Engineer to design, develop, and deploy cutting-edge AI-powered applications. This role will apply machine learning and Generative AI techniques to improve incident prediction, root cause analysis, noise reduction, and operational efficiency. Additionally the role works closely with SRE, Infrastructure, ITSM, and Platform teams to embed AI solutions into production environments.


What You Bring

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field

• 4–7 years of hands-on experience as a software engineer working on AI-focused, cloud-native applications, with exposure to production-grade platforms

Strong fundamentals in computer science, including:

• Algorithms and data structures
• Object-oriented programming and design patterns
• Multi-threaded and distributed systems
• Scalable and resilient software design principles

Proven experience in cloud-native development, building and deploying applications on AWS and/or Azure

• Strong coding and debugging skills with hands-on experience in:
• Python (primary for AI/ML and AIOps use cases)
• Java / JavaScript / TypeScript / GoLang for backend services and integrations

Hands-on experience designing and developing REST APIs and microservices using frameworks such as:

• Flask, FastAPI, Spring Boot, Node.js

Experience building scalable backend services and integrating with modern frontend frameworks such as React, Angular, or Vue (UI experience is a plus)

Strong background in Machine Learning and Deep Learning, including:

• Proficiency in Python, TensorFlow, PyTorch, Hugging Face
• Experience training, fine-tuning, and optimizing models for real-world use cases
• Hands-on experience with Generative AI and LLM ecosystems, including:
• GenAI frameworks such as LangChain, LlamaIndex

Gentic frameworks like AutoGen, Semantic Kernel, CrewAI, PromptFlow, LangFlow, LangGraph

Strong understanding of transformer architectures, embeddings, and inference optimization

Exposure to advanced generative techniques such as GANs and VAEs, with the ability to design scalable and efficient AI systems

Practical experience with prompt engineering, fine-tuning strategies, and RAG-based

architectures for enterprise use cases

Experience working with databases and data stores, including:

• Relational (SQL) and NoSQL databases
• Vector databases and Graph databases for GenAI and AIOps scenarios
• Strong experience with DevOps and cloud-native tooling, including:
• Containerization and orchestration: Docker, Kubernetes
• CI/CD and platform components: Argo, Helm, etcd, Envoy
• Familiarity with MLOps / LLMOps practices and tools such as MLflow, Kubeflow, SageMaker, including model lifecycle management and monitoring

Prior experience deploying AI-driven applications into production environments, ensuring scalability, reliability, and security

Exposure to event-driven architectures and streaming platforms such as Kafka, with a good understanding of pub-sub systems (bonus)

Strong collaboration and communication skills, with experience working in global, cross-functional teams

Key Responsibilities

Generative AI Development
- Research, fine-tune, and deploy generative AI & LLM models for specific use cases.
- Design and implement solutions for text generation, image synthesis, conversational AI, and more.
- Optimize AI models for performance, scalability, and real-time applications.

Backend Development
- Build robust APIs and microservices to integrate AI models into web and mobile applications.
- Develop efficient data pipelines for model training and inference.

Work with databases (SQL/NoSQL) Vector DB/Graph DB to handle structured and unstructured data
- Ensure backend systems are secure, scalable, and capable of handling AI workloads.

Cloud and Deployment
- Deploy AI models and full-stack applications on cloud platforms (AWS, Azure).
- Implement MLOps practices for model lifecycle management, monitoring, and retraining.
- Use containerization and orchestration tools (Docker, Kubernetes) to ensure scalability and reliability.