As a Software Engineer with AI/ML Platform Experience you will:

• Architect and design scalable Kubernetes platforms supporting both traditional ML and Large Language Models (LLMs).
• Provide client support for hosting AI/ML workload on Kubernetes platform powered by GPU
• Lead the development of end-to-end ML pipelines, including data ingestion, model training, evaluation, and deployment.
• Drive AIOps initiative across Middleware platform by collaborating with multi-functional teams, including SRE, Software Engineers to operationalize and optimize ML models effectively.
• Define and implement MLOps standard methodologies such as monitoring, logging, and automated maintenance of models in production.
• Develop infrastructure automation tools and frameworks to improve efficiency across teams.
• Ensure platform reliability, scalability, and performance through meticulous engineering practices.
• Conduct code reviews, establish standard processes, and mentor junior engineers.
• Stay updated on the latest trends in AI/ML to influence platform enhancements.

Minimum Qualifications / Requirement -

• Experience: 5–7 years of software engineering experience, including at least 2+ years in machine learning-related roles.
• Expertise in Golang or Python, with hands-on experience with Kubernetes platform
• Along with ML frameworks (TensorFlow, PyTorch).
• Consistent track record in designing and deploying scalable machine learning systems in production.
• Deep understanding of ML algorithms, data pipelines, and optimization techniques.
• Experience building CI/CD pipelines for ML workflows, including model monitoring and retraining.
• Proficiency in cloud platforms and orchestration tools for distributed systems.
• Strong problem-solving and debugging skills for complex, large-scale systems.
• Experience in mentoring engineers and driving technical decision-making.

Preferred Qualifications / Requirements -

• Kubernetes and Container Orchestration:
• Sophisticated understanding in Kubernetes for managing production-grade systems and ensuring scalability.
• Sophisticated experience with Docker and orchestration of complex services.
• Software development:
• Expertise in Golang or Python
• Develop & enforce secure software development lifecycle
• MLOps Tools and Frameworks:
• Experience with architecting and optimizing workflows using Kubeflow pipelines, KServe, Airflow, and MLflow.
• Ability to design and implement efficient CI/CD pipelines for ML systems.
• Large Language Models (LLMs):
• Understanding of LangChain and experience designing RAG systems.
• Knowledge of integrating and scaling vector databases (e.g., Pinecone, FAISS) for real-world applications.
• Distributed Systems and Microservices:
• Consistent record of designing and leading the development of distributed systems.
• Experience with implementing robust inter-service communication patterns and solving scalability issues.