Senior AI Application Engineer

Technical Expertise:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science or related technical field. 3+ years of machine learning engineering experience, with demonstrated expertise in developing and deploying ML models, implementing best practices in training, evaluation and inference.
• 2+ years of experience in integrating LLM while building AI Agent powered applications.
• Experience in implementing MLOps practices, including CI/CD pipelines, monitoring systems and production deployment of AI/ML models.
• Track record of developing AI-driven applications, including API integrations and model-serving architectures.
• Expert level experience in Python, deep learning frameworks (PyTorch, TensorFlow), LangGraph, LangChain.
• Hands-on experience in software development using Java, Spring Boot, with strong grounding in data structures, algorithms and their application in solving complex engineering challenges.
• Proficiency with cloud services (AWS, Azure, Oracle Cloud), containerization tools (Docker, Kubernetes) and microservices architecture.
• Experience in designing and maintaining, scalable microservices in an enterprise environment.
• Proven ability to rapidly learn new technologies, prototype solutions and independently design and implement application components.

Soft Skills & Leadership

• Proven ability to drive technical outcomes, take ownership of deliverables, and work independently in fast-evolving AI solution spaces.
• Strong communication skills, with the ability to articulate technical concepts, document solution approaches and collaborate across distributed teams.
• Demonstrated problem-solving ability when working with complex AI workloads, distributed systems and cloud-native application behaviours.
• A proactive, experimentation-oriented mindset with a strong willingness to learn emerging AI technologies, frameworks and engineering patterns.

Key Responsibilities
I. Cloud Application Development

• Design and develop cloud-native application services on Kubernetes using Java, Python and Spring Boot.
• Integrate application components with OCI services, including GenAI Service, Object Storage and API Gateway.

II. AI, LLM and Agentic Systems

• Implement AI-powered capabilities using LLM, prompt engineering, and agentic frameworks such as OpenAI Agent SDK or LangGraph.
• Build RAG workflows including embeddings, indexing and hybrid search.

III. DevOps and Deployment

• Support CI/CD processes and containerized deployment workflows using Podman, Docker and OKE.
• Troubleshoot application and runtime issues across distributed environments.

IV. Collaboration and Knowledge Sharing

• Work with cross-functional engineering teams to align solution designs with business requirements.
• Conduct independent research on emerging AI technologies, tools, and engineering patterns to introduce improvements and new solution approaches.
• Share knowledge, mentor peers and contribute to internal enablement of AI and cloud development best practices.

Career Level - IC3