What You'll Do Here :

- Architect and design scalable, efficient AI systems and reference architectures for rapid iteration.

- Lead the Agile AI build path from DemoBytes , POC , MVP , Demoable , Production, ensuring clear stage gates and documentation.

- Drive a fast release cadence : monthly demo drops and production releases every two months.

- Integrate AI services and LLMs into existing platforms, data pipelines, and APIs with reliability and security in mind.

- Establish best practices for model evaluation, testing, observability, and CI/CD in MLOps.

- Mentor engineers on modern AI stacks, code quality, and experiment tracking.

- Champion ethical AI, privacy-by-design, and compliance in data handling and deployments.

- Track the AI landscape and propose pragmatic adoption of new models, tools, and methodologies.

Who We'd Love to Work With :

We'd love it if you bring a mix of :

- 6+ years in AI/ML engineering/architecture with a strong record of rapid prototyping , production.

- Deep hands-on with Python and modern ML/DL frameworks : TensorFlow, PyTorch, Keras.

- Experience with LLMs and related tooling (prompting, RAG, fine-tuning, evaluation).

- Comfort with platforms/tools such as Hugging Face, scikit-learn, spaCy, NLTK, Gensim, YOLO, FastAI, and experiment tracking (Weights & Biases).

- Familiarity with WatsonX or similar enterprise AI platforms is a plus.

- Exposure to AI dev environments like Cursor, Windsurf, and AI coding copilots; familiarity with Claude or similar LLMs.

- Ability to translate complex AI concepts into practical designs for engineers, PMs, and business stakeholders.

- Strong communication, documentation, and collaboration chops.

- Bonus points if youre familiar with MLOps stacks (Docker, Kubernetes, model registries), vector databases, retrieval pipelines, and evaluation frameworks.

If youre curious, driven, and love solving challenges with creativity and logic, you'll fit right in.