Machine Learning Engineer

Purple Drive

Machine Learning Engineer Share Contractual Newark, NJ

Role: Machine Learning Engineer

As a Machine Learning Engineer, you will play a critical role in designing, developing, and deploying advanced machine learning solutions that drive innovation and create measurable business value. You will collaborate closely with business stakeholders, data scientists, and cross-functional engineering teams to transform ideas into scalable, production-ready systems. This role requires both strong technical expertise and leadership ability to guide a small team and deliver impactful solutions.

Key Responsibilities

• Lead and deliver machine learning projects from inception through production, building strong relationships with business partners and cross-functional teams.
• Collaborate with business leaders and subject matter experts to define success criteria and optimize products, features, and models.
• Partner with data scientists to design, implement, and train machine learning models.
• Work with infrastructure teams to enhance architecture, scalability, stability, and performance of ML platforms.
• Design and optimize data pipelines to support high-performance ML model training and inference.
• Extend and customize machine learning libraries and frameworks for project-specific needs.
• Define and implement model monitoring, governance, and operationalization processes for ML solutions.
• Establish objectives and own the technical roadmap for ML platforms, ensuring delivery of results.
• Define and promote standards of engineering and operational excellence for ML systems.
• Apply architectural best practices in the delivery of data science and AI solutions.

Required Skills & Experience

• Strong background in software engineering with proven experience as a Machine Learning Engineer.
• Bachelor's degree in Computer Science, Computer Engineering, or related field; Master's preferred.
• Advanced proficiency in Python, Java, and Scala with solid CS fundamentals (algorithms, data structures, multithreading).
• Hands-on experience with Generative AI, LangChain, and RAG-based techniques.
• Expertise in ML/DL libraries such as XGBoost, Scikit-learn, TensorFlow, and PyTorch.
• Experience building and deploying ML solutions on public clouds (AWS, GCP).
• Familiarity with ML platforms such as SageMaker, H2O, and DataRobot.
• Strong knowledge of the ML lifecycle, including containerization, batch vs. real-time inference, and application security.
• Proven track record in developing and deploying production-grade ML applications with cloud-based automation pipelines.
• Experience working in Agile/Scrum environments with multiple stakeholders.
• Excellent communication, collaboration, and problem-solving skills with thought leadership and innovative thinking.

Nice-to-Have

• Experience with search platforms (e.g., Solr, Elasticsearch).
• Hands-on experience building recommender systems.
• Exposure to graph databases (e.g., Neo4j).
• Familiarity with CI/CD tools (e.g., Jenkins).
• Domain knowledge in Financial Services, Insurance, or 401K.
• AWS Solutions Architect certification.