Ci - Ssr. Ai Engineer - 181

We are seeking a highly skilled and motivated Senior AI Engineer to join our Continuous Integration (CI) team.
The AI Engineer will play a pivotal role in designing, developing, and deploying AI-driven microservices that power our next-generation enterprise platform.
This role is critical to advancing our AI capabilities by leveraging cutting-edge frameworks such as Langchain and LangGraph, and by implementing scalable, maintainable solutions within a microservice architecture.
The ideal candidate will bring deep expertise in containerization, orchestration, and multi-agent systems, contributing to the robustness and efficiency of our AI infrastructure.
This position offers an exciting opportunity to work at the intersection of AI innovation and cloud-native technologies, collaborating closely with cross-functional teams to drive continuous integration and deployment excellence.
Responsibilities
Design, develop, and deploy AI-driven microservices using Python and advanced AI frameworks including Langchain and LangGraph.
Architect and implement scalable multi-agent systems that enhance the intelligence and responsiveness of our enterprise platform.
Utilize containerization technologies such as Docker to package AI microservices, ensuring consistency across development, testing, and production environments.
Manage orchestration of containerized applications using Kubernetes, including programmatic handling of deployments through Kubernetes APIs.
Collaborate with DevOps and platform teams to integrate AI microservices into continuous integration and continuous deployment (CI/CD) pipelines, ensuring rapid and reliable delivery.
Implement and maintain MCP Reverse Proxy configurations to optimize routing, security, and load balancing for AI services within the enterprise deployment architecture.
Contribute to the design and deployment of enterprise-grade AI solutions that align with organizational goals and compliance standards.
Work closely with data scientists, software engineers, and product managers to translate AI research into production-ready services.
Monitor, troubleshoot, and optimize AI microservices performance, scalability, and reliability in cloud environments.
Participate in code reviews, knowledge sharing, and mentoring of junior engineers to foster a culture of technical excellence.
Stay abreast of emerging AI technologies, container orchestration trends, and best practices to continuously improve the AI platform.
Python: Proficient in Python programming, with experience in developing AI applications and microservices.
Ability to write clean, efficient, and maintainable code.
Langchain: Expertise in Langchain framework for building AI applications that integrate language models with external data and tools.
LangGraph: Experience with LangGraph for constructing and managing graph-based AI workflows and decision-making processes.
Microservice Architecture: Strong understanding of microservice design principles, including service decomposition, API design, and inter-service communication.
Multi-agent Systems: Proven experience in designing and deploying multi-agent AI systems that enable autonomous, collaborative, or competitive agent behaviors.
MCP Reverse Proxy: Knowledge of MCP Reverse Proxy configurations and management to facilitate secure and efficient routing of AI microservices.
Enterprise Platform Deployment: Familiarity with deploying AI solutions within enterprise-grade platforms, ensuring scalability, security, and compliance.
Docker and Kubernetes (K8s) Experience: Hands-on experience with containerization using Docker and orchestration with Kubernetes, including deployment, scaling, and management of containerized AI services.
Nice-to-Have Skills
Application-to-Application (A2A) Integration: Experience integrating AI microservices with other enterprise applications to enable seamless data and process flows.
Advanced Embedding Strategies: Knowledge of embedding techniques to represent complex data structures and semantic information for AI models.
Fine-Tuning: Experience fine-tuning large language models or other AI models to improve performance on domain-specific tasks.
Evaluations: Ability to design and conduct rigorous evaluations of AI models and systems to ensure quality and effectiveness.
Scaling with Tool Calling: Familiarity with scaling AI workflows by orchestrating external tool calls and managing dependencies.
Programmatic Handling of Kubernetes Deployments through Kubernetes APIs: Advanced skills in automating Kubernetes operations using APIs and custom controllers.
Sandboxed Environments for Ephemeral Code Execution: Experience creating secure, isolated environments for running transient AI code safely.
Apache Kafka: Knowledge of event streaming platforms like Apache Kafka to support event-driven architectures and real-time data processing.
Event Driven Architectures: Understanding of designing AI systems that react to events asynchronously for improved responsiveness and scalability.
Caching Large Language Model Responses: Techniques for caching AI model outputs to reduce latency and computational costs.
Large Language Model Memory: Experience managing memory and context in large language models to enhance conversational AI capabilities.
Rule-Based Decision Making: Ability to implement rule-based logic to complement AI-driven decision processes.
Graph-Based Decision Making: Expertise in leveraging graph structures for complex decision-making and knowledge representation.
Swarm Architectures: Familiarity with swarm intelligence concepts to coordinate multiple AI agents in distributed environments.
#J-18808-Ljbffr