Software Engineering Technical Leader - AI/ML Platform

• 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 along with Re-ranking and Embedding as a Service (RAGaaS)
• 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 across organization 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.

• Experience : 10+ years of software engineering experience, including at least 2+ years in AI & machine learning-related roles.
• Expertise in Golang or Python , with hands-on experience with Kubernetes platform along with ML frameworks (TensorFlow, PyTorch).
• Drive Architecture discussion with regards to software development, building AI/ML supporting platforms & services such as Kubernetes platform, Re-ranking & embedding as a service [RAGaaS]
• Subject Matter expertise with microservice architecture, SSO and containerized cloud applications.
• 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.

• Kubernetes and Container Orchestration:
• Expert understanding in Kubernetes for managing production-grade systems and ensuring scalability.
• Expert with Docker and orchestration of complex services.

Software development:

• Expertise in Golang or Python
• Develop & enforce secure software development lifecycle

MLOps Tools and Frameworks:

• Strong hand-on 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):

• Experience of LangChain and experience designing RAG systems.
• Experience of integrating and scaling vector databases (eg, Pinecone, FAISS) for real-world applications.

Distributed Systems and Microservices:

• Consistent track record of designing and leading the development of distributed systems.
• Experience with implementing robust inter-service communication patterns and solving scalability issues.