- 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.