Note: The job is a remote job and is open to candidates in USA. Vytwo Technologies Inc. is seeking a highly skilled Data Scientist to join their team, focusing on both structured and unstructured data domains. The role involves building and optimizing machine learning models, developing NLP pipelines, and utilizing modern big data tools and cloud services for scalable data science solutions.
Responsibilities
- Build, deploy, and optimize ML models for predictive analytics, forecasting, classification, and regression
- Perform large-scale feature engineering using PySpark and Big Data tools
- Work on batch pipelines, model versioning, and experiment tracking
- Develop cost estimation and risk/likelihood models using statistical and ML techniques
- Build NLP pipelines using deep learning frameworks such as PyTorch, TensorFlow, or similar
- Develop real‑time, low‑latency inference systems for text classification, embeddings, semantic search, summarization, and retrieval
- Create prompts, context graphs, and agentic workflows for LLM-based systems
- Apply knowledge of prompt engineering, context engineering, and autonomous agent frameworks to production systems
- Work in Databricks for ETL, feature engineering, ML training, and orchestration
- Use Azure services for model deployment, data pipelines, and infrastructure
- Collaborate using Git-based workflows; leverage tools like GitHub Copilot, Claude Code, etc
- Implement model monitoring, observability, drift detection, and performance tracking
Skills
- Strong hands-on experience with Databricks (Delta Lake, MLflow, Job Orchestration)
- Excellent PySpark skills for large-scale distributed data processing
- Proficiency in Azure cloud services (ADF, Azure ML, AKS, Databricks on Azure)
- Strong understanding of ML algorithms, statistical methods, and data analysis
- Experience with deep learning frameworks: PyTorch, TensorFlow, Transformers (HuggingFace)
- Experience with model monitoring and ML observability
- Ability to write clean, optimized code and leverage AI code assistants
- Prompt engineering (task prompts, chain of thought, tool calling, retrieval prompts)
- Context engineering (retrieval pipelines, RAG, memory management, context structuring)
- Knowledge of LLM-based agentic frameworks (LangChain, Semantic Kernel, CrewAI, AutoGen, etc.)
- Experience with vector databases and embedding models
- Experience with containerization (Docker, Kubernetes, AKS)
- Experience deploying models to production (REST APIs, real-time endpoints)
- Knowledge of streaming technologies (Kafka, EventHub, Spark Streaming)
- Understanding of CI/CD for ML (Azure DevOps / GitHub Actions)
Benefits
- Flexible work from home options available.
Company Overview