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(ref : hirist.tech)