AI Architect

Job Title: AI Architect Company: Everest Global Solutions Location: Jersey City, NJ (On-site) Employment Type: Contract Salary: $150,000 - $200,000/year

Job Description:

Role Overview We are seeking an AI Architect with a background in Pharma/Life Sciences. Please note that this role focuses on AI Architect profiles rather than Senior/Lead Data Scientist profiles.

Key Responsibilities & Requirements

• Core Expertise: Experience in Data Science, Machine Learning, Artificial Intelligence, or related fields.
• Technical Proficiency: Strong proficiency in SQL (preferred) and Python.
• End-to-End ML Lifecycle: Hands-on experience in data analysis, feature engineering, model development, evaluation, and deployment of machine learning solutions to solve business problems.
• Generative AI Knowledge: Good understanding of Generative AI concepts, including Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and emerging AI frameworks and protocols such as MCP.
• Data & Collaboration: Ability to work with structured and unstructured data, build scalable AI/ML solutions, and collaborate with cross-functional teams to translate business requirements into data-driven outcomes.
• Soft Skills: Strong problem-solving, analytical, and communication skills, with a demonstrated willingness and enthusiasm to learn new technologies, tools, and emerging AI/ML trends in a fast-evolving landscape.

Preferred Qualifications

• Snowflake: Experience with the Snowflake data platform; exposure to Snowflake AI capabilities and Cortex services is a good-to-have skill.
• MLOps: Familiarity with MLOps, model deployment, monitoring, and AI application lifecycle management.
• Data Engineering: Knowledge of data warehousing, data engineering concepts, and API integrations.
• GenAI Development: Exposure to GenAI application development, vector databases, semantic search, and AI orchestration frameworks.
• Industry Experience: Lifesciences experience is preferred.

Primary Skills

• Statistical Analysis: Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Statistical analysis and computing, Probabilistic Graph Models, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Distance metrics (Hamming Distance, Euclidean Distance, Manhattan Distance).
• Programming & Tools: Python/PySpark, SAS/SPSS, R/R Studio, Great Expectation, Evidently AI.
• Machine Learning Frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet.
• ML Techniques: Classification (Decision Trees, SVM), Tools (KubeFlow, BentoML).

Specialization Data Science Advanced: Senior AI/ML Engineer