Responsibilities
- Architect and Build AI Solutions: Design and architect end-to-end platform capabilities and services that integrate with Generative AI technologies.
- Implement RAG Solutions: Develop and deploy solutions using VectorRAG and GraphRAG techniques with both open-source and commercial Large Language Models.
- Develop AI Workflows: Apply a strong understanding of Agentic AI workflows and frameworks to build sophisticated, automated systems.
- Drive DevOps Best Practices: Implement robust CI/CD pipelines, automated testing, and containerization (Docker, Kubernetes) to streamline the development and deployment of machine learning models.
- End-to-End Project Ownership: Manage your deliverables from conception to deployment, proactively identifying and mitigating risks to ensure project goals are met.
- Rapid Prototyping: Perform and lead quick proof-of-concept (POC) projects to evaluate and demonstrate the feasibility of new GenAI use cases.
- Ensure System Integrity: Appropriately assess and manage risk in all technical and business decisions, ensuring compliance with applicable laws, rules, and regulations, and maintaining transparency in reporting and managing control issues.
- Collaborate and Innovate: Work closely with cross-functional teams to solve complex problems, adapt quickly to changing priorities, and contribute to a culture of innovation.
Basic qualifications
- Experience: 5+ years of relevant experience in Software Development or a Systems Analysis role, with a strong focus on machine learning.
- Programming Skills: Strong object-oriented programming skills with expert-level proficiency in Python.
- AI/ML Expertise: Deep experience with AI/ML frameworks (e.g., Langchain) and hands-on experience with Generative AI technologies and Large Language Models.
- Application Development: Proven experience building applications that apply LLMs and GenAI to use cases such as search, chat agents, and guided analytics.
- DevOps Knowledge: Strong, working knowledge of DevOps tools (e.g., Jenkins, GitLab, Docker, Kubernetes) and Infrastructure as Code (IaC) practices.
- Architectural Understanding: Solid understanding of modern software development practices, including microservices architecture and API design principles.
- Data Proficiency: Experience with various data storage solutions (e.g., NoSQL, SQL, data lakes) and data pipeline orchestration.
- Problem-Solving: Excellent analytical and problem-solving skills with a proven ability to troubleshoot complex technical issues and provide innovative solutions.
Preferred qualifications
- Cloud Architecture: Experience with various cloud platforms (e.g., AWS, GCP, Azure) and architectures (e.g., single/multi-tenant).
- Large-Scale Systems: Experience designing and implementing microservices-based architectures for large-scale platforms in complex environments.
- Leadership: Demonstrated leadership, project management skills, and a proven track record of self-motivation and continuous learning.
Tags & focus areas
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