Requirements
- 8+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production.
- Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads.
- Prior experience working directly with clients in use case discovery, product development, and leading client engagements.
- Technical Expertise: Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development.
- Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications.
- Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
- Familiarity with Kubernetes and other container management tools.
- Ability to write well-structured, organized code and automated unit/E2E tests.
- Comfortable with polyglot persistence models (SQL vs. NoSQL).
- ML Operations: Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
- Problem Solving: Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges.
- Communication: Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers.
