Mandatory Skills

GenAI, LLMs, RAG, Python, Java, or Scala, REST APIs, Kafka, and cloud platforms (AWS/Azure)

Desired Skills

Docker, Kubernetes, CI/CD, Git, and exposure to prompt engineering, embeddings, or AI-based toolchains preferred

Years of exp needed

 5+

Roles and Responsibilities post joining

Designing and implementing backend systems to support Generative AI workflows and services.
Developing microservices and APIs that integrate with GenAI models, vector databases, and orchestration tools (e.g., LangChain, LangGraph).
Building scalable infrastructure for deploying and serving LLMs and AI pipelines in production environments.
Creating and maintaining data pipelines to support GenAI use cases, including prompt data processing and model feedback loops.
Participating in architecture reviews focused on optimizing GenAI system performance, latency, and modularity.
Collaborating with AI/ML engineers to enable plug-and-play components (e.g., Intent Resolver, Tool Agent, LLM Gateway) in agentic workflows.
Debugging, profiling, and optimizing GenAI applications, including prompt execution paths, model inference, and external API integration.

Writing technical documentation related to GenAI service design, deployment procedures, and best practices.

Details about the project they would be working on.
(if there is differentiator in terms of superior technology versions/exposure/other benefits please specify. It will help generate more interest and boost applications)

• 5+ years of total experience in IT

• 1+ years of hands-on experience with GenAI technologies — including LLMs, prompt engineering, embeddings, fine-tuning, or building RAG (Retrieval-Augmented Generation) pipelines.
• Strong programming skills in one or more languages such as Python, Java, or Scala, with a solid foundation in software design principles, testing, and version control (e.g., Git). Python developers are preferred.

• Strong backend engineering background with expertise in building scalable systems using Python, C#, Java or similar, and hands-on experience with microservices, REST APIs, Kafka, and cloud platforms (AWS/Azure) — preferably with exposure to secure, low-latency architectures.
• Demonstrated interest or experience in GenAI/Agentic AI systems — including building or integrating with LLMs (e.g., OpenAI, Claude), use of tools like LangChain, LangGraph, Copilot, or vector DBs, or implementing AI agent orchestration logic in real-world workflows.

• Comfortable working in AI-first environments, with experience using modern developer tools (e.g., Docker, Kubernetes, CI/CD, Git), and exposure to prompt engineering, embeddings, or AI-based toolchains preferred.