Description :

About the Role :

Were hiring an engineer who blends large-language-model know-how, data-platform chops, and product sense. You will architect the AI stack from ingestion pipelines through model deployment, standup a credit-aware inference platform, and integrate language models into both customer-facing features and internal tools.

Key responsibilities :

- Own the AI roadmap translate business priorities into model, data, and infrastructure milestones.

- Build data & ML infrastructure design data lake, feature store, vector search (pgvector), model registry, and CI/CD for ML.

- Develop and deploy models train or fine-tune models the domain data; serve them behind low-latency, cost-controlled APIs.

- Ensure quality, cost, and compliance set up automated evaluation, token-spend monitoring, and GDPR-safe data flows.

Skills and qualifications :

- 6 to 10 years in back-end or full-stack engineering with at least one Gen-AI product or workflow in production.

- Strong in Java and fluent in Python or Node for ML tooling.

- Practical experience with LLMs, embeddings, vector search, and retrieval-augmented generation.

- Deep familiarity with AWS or GCP services, container orchestration, CI/CD, and monitoring.

- Comfortable setting up data models and MLOps processes (model registry, drift alerts, blue-green model deploys).

- Proven leadership in code reviews, technical mentoring, and cross-functional communication