Data Scientist

About the Role

Key Responsibilities

• Build and operationalize machine learning pipelines using Python (PyTorch, TensorFlow, scikit-learn) for classification, summarization, embeddings, and semantic search use cases.
• Develop solutions leveraging ChatGPT-3.5 and GPT-4.0, integrating OpenAI APIs via LangChain and LangSmith.
• Design structured prompt templates, dynamic chaining logic, and retrieval-augmented generation (RAG) flows using vector databases such as Pinecone or FAISS.
• Deploy models using Azure Machine Learning (AML), MLflow, and Docker.
• Configure AKS or Azure Container Apps for scalable inference.
• Apply CI/CD via Azure DevOps and GitHub Actions for ML workflows.
• Finetune foundation models using domain-specific datasets.
• Apply techniques such as LoRA, PEFT, and quantization to optimize performance and latency.
• Build data ingestion and transformation pipelines using Azure Data Factory, Pandas, and Spark.
• Design custom feature stores and embedding pipelines using Hugging Face Transformers.
• Implement model monitoring with Prometheus, OpenTelemetry, and Azure App Insights.
• Track performance, bias, and drift using custom evaluation dashboards.
• Enforce secure data handling practices, PII redaction, and token-level access to AI APIs.
• Follow enterprise MLOps security standards and responsible AI guidelines.

Requirements

• Bachelor's, Master's, or PhD in Computer Science, AI/ML, Data Science, or a related technical field.
• 5+ years of experience building and deploying production-grade ML models and APIs.
• 2+ years of hands-on experience with large language model (LLM) integration using OpenAI, Cohere, Anthropic, or open-source models (e.g., LLaMA, Falcon, Mistral).
• Proficiency in Python, with strong knowledge of PyTorch, Transformers (Hugging Face), and LangChain-based app development.
• Experience working with cloud-native ML services and MLOps stacks on Azure (AML, Blob Storage, Azure Functions, App Services).
• Familiarity with RESTful API development, asynchronous processing (e.g., Celery, FastAPI), and container orchestration (Docker, Kubernetes).
• Solid grounding in modern ML techniques (vector search, attention models, embeddings), with practical knowledge of evaluation metrics and deployment best practices.
• Strong debugging, documentation, and collaboration skills in a hybrid Agile/Scrum environment.

Nice to Have

• Experience with OpenAI APIs and LangChain or similar frameworks.
• Knowledge of enterprise security standards and responsible AI practices.
• Experience with Prometheus, OpenTelemetry, and Azure monitoring tools.

Qualifications

• Educational background in Computer Science, AI/ML, Data Science, or related fields.
• Experience with large language models and cloud-native ML services.

Benefits & Perks

• Salary range of $150,000 to $170,000 or hourly equivalent, based on experience and qualifications.
• Comprehensive healthcare package (medical, dental, vision).
• Disability and group term life insurance.
• Health and flexible spending accounts.
• Utilization bonus.
• 401(k) with match.
• Flexible time off for salaried employees.
• Parental leave for salaried employees.
• Flexible work arrangements.