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- AI/ML Full Stack Engineer (Mid Level)
- Job Description
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**Job title**
AI/ML Full Stack Engineer (Mid Level)
**Role Summary**
Build and maintain full‑stack predictive intelligence applications. Develop data pipelines, deploy ML models, and create user‑facing interfaces that deliver forward‑looking insights. Work across backend, frontend, and data engineering to support real‑time, large‑scale analytics.
**Expectations**
- Translate complex business requirements into scalable, reliable technical solutions.
- Produce production‑ready code and models that meet performance, reliability, and security standards.
- Collaborate with cross‑functional teams (product, analytics, engineering) to iterate on solutions.
- Maintain high code quality, continuous integration, and observability of ML services.
**Key Responsibilities**
1. Design, develop, and maintain end‑to‑end predictive applications.
2. Build automated data pipelines for ingestion, transformation, and storage of structured and unstructured data.
3. Train, evaluate, and deploy forecasting, regression, classification, and ensemble models.
4. Integrate large‑scale, real‑time data sources; support time‑series and streaming data.
5. Develop backend services, REST/GraphQL APIs, and integrate with frontend (React/TS).
6. Apply statistical and ML techniques to support decision making and experimentation.
7. Optimize performance, reliability, and scalability of data pipelines and ML services.
8. Ensure monitoring, logging, and CI/CD for continuous delivery and operational stability.
9. Collaborate with product and analytics to validate model outputs and performance.
10. Experiment with modern AI, including large language models and embeddings, to enhance automation.
**Required Skills**
- Python (data processing, modeling, backend).
- JavaScript/TypeScript, React or similar frontend framework.
- SQL, relational/analytical databases.
- REST/GraphQL API design and consumption.
- Cloud platforms (AWS, GCP, Azure) and cloud‑native architecture.
- Machine learning systems: production training, evaluation, deployment.
- Statistics, probability, linear algebra, predictive modeling.
- Time‑series analysis, forecasting, regression, classification.
- Data quality assessment, bias, uncertainty, and model assumptions.
- Problem solving and working in ambiguous technical environments.
**Preferred Skills**
- PyTorch, TensorFlow, or JAX.
- Probabilistic modeling, Bayesian methods, causal inference.
- Large language models, embeddings, vector databases.
- Airflow, Dagster, dbt, or other orchestration tools.
- Streaming or near‑real‑time pipelines.
- Docker, CI/CD pipelines.
- Observability and monitoring for ML systems.
- AdTech/MarTech experience and interest.
- Experimentation frameworks (A/B testing, model validation).
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field.
- 2–4 years of professional software engineering experience with full‑stack exposure.
- Master’s degree or advanced certifications optional.