AI Python Developer - Machine Learning

Job Description : - Develop tooling and self-service capabilities for deploying AI solutions for the firm. Collaborate with other developers to enhance the developer experience when building and deploying AI applications. - Have a platform mindset and build common, reusable solutions to scale Generative AI use cases using pre-trained models as well as fine-tuned models. - Leverage Kubernetes/OpenShift to deploy modern containerized workloads. - Leverage container registries like JFrog Artifactory, container packaging/configuration management technologies like Helm & Kustomize, and GitOps deployment methods to orchestrate, manage, and deploy workloads. - Integrate with capabilities such as large-scale vector stores for embeddings. - Author best practices in the Generative AI ecosystem, including when to use which tools, available models such as GPT, Llama, Hugging Face, etc., and libraries such as LangChain. - Analyze, investigate, and implement GenAI solutions focusing on Agentic Orchestration and Agent Builder frameworks. - Contribute to major design decisions and product selection for building Generative AI solutions, including app authentication, service communication, state externalization, container layering strategy, and immutability. - Ensure AI platform is reliable, scalable, and operational (e.g., blueprints for upgrade/release strategies like Blue/Green; logging/monitoring/metrics; automation of system management tasks). - Participate in all teams Agile/Scrum ceremonies. What you'll bring to the role : - At least 4 years of relevant experience is generally expected. - Strong hands-on application development background in Python. - Broad understanding of data engineering (SQL, NoSQL, Kafka, Redis), data governance, data privacy, and security. - Experience in development, management, and deployment of Kubernetes workloads, preferably on OpenShift. - Experience with designing, developing, and managing RESTful services for large-scale enterprise solutions. - Hands-on experience with multiprocessing, multithreading, asynchronous I/O, or performance profiling in at least one programming language (preferably Python). - Practitioner of unit testing, performance testing, and BDD/acceptance testing. - Understanding of OAuth 2.0 protocol for secure authorization. - Proficiency with observability tools including Grafana, Loki, Prometheus, and Cortex. - Demonstrated experience in DevOps, understanding of CI/CD (Jenkins) and GitOps. - Ability to articulate technical concepts effectively to diverse audiences. - Strong desire and ability to influence development teams and help them adopt AI. - Demonstrated ability to work effectively and collaboratively in a global organization across time zones. - Understanding of deep learning, including Machine Learning frameworks such as TensorFlow or PyTorch. - Understanding of Information Security and secure coding practices. - Experience in building cloud and container-native applications. - Knowledge of DevOps and Agile practices. - Excellent communication skills. - Good knowledge of microservices-based architecture and industry standards for public and private cloud. - Good understanding of modern application configuration techniques. - Hands-on experience with cloud application deployment patterns like Blue/Green. - Good knowledge of various DB engines (SQL, Redis, Kafka, etc.) for cloud app storage. - Experience building AI applications, preferably Generative AI and LLM-based apps. - Deep understanding of AI agents, Agentic Orchestration, multi-agent workflow automation, with hands-on experience in Agent Builder frameworks such as LangChain and LangGraph. - Experience working with Generative AI development, embeddings, and fine-tuning of models. - Understanding of MLOps / LLMOps. - Understanding of SRE techniques. Job Description : - Develop tooling and self-service capabilities for deploying AI solutions for the firm. Collaborate with other developers to enhance the developer experience when building and deploying AI applications. - Have a platform mindset and build common, reusable solutions to scale Generative AI use cases using pre-trained models as well as fine-tuned models. - Leverage Kubernetes/OpenShift to deploy modern containerized workloads. - Leverage container registries like JFrog Artifactory, container packaging/configuration management technologies like Helm & Kustomize, and GitOps deployment methods to orchestrate, manage, and deploy workloads. - Integrate with capabilities such as large-scale vector stores for embeddings. - Author best practices in the Generative AI ecosystem, including when to use which tools, available models such as GPT, Llama, Hugging Face, etc., and libraries such as LangChain. - Analyze, investigate, and implement GenAI solutions focusing on Agentic Orchestration and Agent Builder frameworks. - Contribute to major design decisions and product selection for building Generative A