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
