- Company Name
- Real Chemistry
- Job Title
- Data Science and Machine Learning Engineer
- Job Description
-
**Job title**: Data Science and Machine Learning Engineer
**Role Summary**: Design, develop, test, and deploy scalable AI/ML models to solve business problems. Work across the ML lifecycle—data preparation, modeling, experimentation, MLOps, and production—collaborating with data engineers, product managers, and stakeholders to translate requirements into high‑impact AI solutions.
**Expactations**:
- Deliver production‑ready models that meet performance, latency, and compliance standards.
- Continuously improve model accuracy and robustness through experimentation and monitoring.
- Communicate technical findings clearly to non‑technical audiences.
- Adhere to ethical AI and data governance guidelines.
**Key Responsibilities**:
- Build, train, evaluate, and tune supervised, unsupervised, and generative models.
- Develop end‑to‑end ML pipelines, including feature engineering, training, and inference.
- Experiment with LLMs, RAG frameworks, and agentic AI systems; document results and iterate.
- Implement MLOps workflows (CI/CD, monitoring, drift detection) using tools like MLflow, SageMaker, or Kubeflow.
- Deploy models to cloud platforms (AWS, Azure, GCP) with containerization and orchestration.
- Collaborate with product and engineering teams to define problem framing and technical requirements.
- Maintain scalable, low‑latency inference environments and ensure observability.
**Required Skills**:
- **Programming**: Python, deep experience with TensorFlow, PyTorch, Scikit‑learn, or Keras.
- **Cloud & MLOps**: Proven deployment on AWS, Azure, or GCP; familiarity with MLflow, SageMaker, Vertex AI, or Kubeflow.
- **Data & Analytics**: SQL, data manipulation, statistical analysis, and experiment design (A/B testing).
- **AI Technologies**: Generative AI (LLMs), RAG pipelines, vector databases, embeddings.
- **Soft Skills**: Strong analytical mindset, problem‑solving, ability to explain complex concepts to non‑technical stakeholders, collaboration in fast‑paced teams.
**Required Education & Certifications**:
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related field.
- 3–7 years of applied experience in data science, ML engineering, or AI model development.
- Relevant certifications (e.g., AWS Certified Machine Learning, GCP Professional Data Engineer, Azure AI Engineer) are a plus.