Machine Learning Engineer

Title: ML Engineer

Location: 100% Remote (PST hours)

Duration: 12 Months Contract

Open for H4EAD, L2S, GC and USC on W2

Recent Healthcare Experience is Mandatory.

Job Purpose:

The Machine Learning Engineer designs, implements, configures, and maintains advanced analytics environments to solve complex business challenges. This role supports the development of strategies and architectural designs for implementing Advanced Analytics, Big Data, Machine Learning, Natural Language Processing, Generative and Agentic AI solutions, while managing AI and MLOps practices.

Job description:

Architect and build scalable ML pipelines using Databricks (PySpark, MLflow) and Snowflake (Snowpark, Streamlit, Cortex AI)

Design and implement RAG and LLM-based solutions for enterprise data intelligence and automation

Develop supervised (classification, regression) and unsupervised ML models for diverse business use cases

Lead MLOps implementation including model versioning, CI/CD, automated testing, and monitoring (MLflow, Azure DevOps)

Deploy and scale NLP/ML models into production with robust CI/CD pipelines and cross-team collaboration

Build and optimize data pipelines for structured and unstructured data (Azure Data Factory, PySpark, SnowSQL)

Tune Databricks and Snowflake environments for performance and scalability of AI/ML workloads

Provide technical leadership and mentorship on ML best practices, LLM development, and cloud-native workflows

Drive ML architecture, evaluate emerging technologies, and align solutions with enterprise goals

Define development standards, collaborate with stakeholders, and architect enterprise-wide advanced analytics and data platforms

Skills Needed:

Expertise in Snowflake architecture, performance tuning, and SQL optimization.

Experience with ETL/ELT tools (e.g., Azure Data Factory, Coalesce) and building scalable data pipelines.

Experience with LLM development (e.g., RAG, document summarization, chatbot integration using LangChain or LlamaIndex).

Strong understanding of data warehousing, dimensional modeling, and cloud platforms (Azure, AWS, GCP).

Proficiency in Python, SnowSQL, and automation using Snowflake’s API and Snowpark.

Knowledge of BI tools (e.g., Power BI, Tableau, SSRS) and ML integration using Snowpark, Azure Machine Learning, Databricks, and Snowflake Streamlit.

Proficiency in MLOps, including MLflow, Azure DevOps, model monitoring, and alerting.

Familiarity with CI/CD pipelines and version control using Git.

Strong background in data science, machine learning, deep learning, and advanced statistical techniques.

Deep understanding of ML system design and industry-standard integration patterns for production AI.

Excellent written and verbal communication skills.

Experience in Deep Learning Libraries, such as tensorflow or pytorch is perferred.