Lead ML Engineer - Newark NJ

As a Lead, Machine Learning Engineer, you will be leading the engineering of Agentic AI

systems and partner with Data Scientists, Data Engineers, Data Analysts, Dev Sec Ops  and

other professionals to implement machine learning models and Agentic AI systems that

will deliver stability, producibility, scalability and integration with other products and

services. You will implement capabilities to solve sophisticated business problems,

deploy innovative products, services and experiences to delight our customers! In addition

to advanced technical expertise and experience, you will bring excellent problem solving,

communication and teamwork skills, along with agile ways of working, strong business

insight, an inclusive leadership attitude and a continuous learning focus to all that you do.

• Architect, Design and Development and Deployment of ML models and Agentic AI systems that solve real-world business problems, while working in collaboration

with the Product and Data Science teams; remove complex technical impediments

• Solve complex problems by writing and testing application code, developing and validating ML models and Agentic AI, and automating tests and deployment using
• Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
• Construct optimized data, ingestion and memory pipelines to feed ML models and Agentic systems
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML

models and application code

• Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day-to-day
• Work on complex problems in which analysis of situations or data requires and evaluation of intangible variables.
• Use programming languages including but not limited to Python, C++, SQL

• Bachelor of Computer Science or Engineering or experience in related fields
• Ability to coach others with minimal guidance and effectively leverage diverse

ideas, experiences, thoughts and perspectives to the benefit of the organization

• Experience with agile development methodologies and Specification-Driven Development (SDD)
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
• Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges
• Excellent problem solving, communication and collaboration skills
• Advanced experience and/or expertise with several of the following:
• Software Engineering & System Design:
  Requirement analysis, coding, and testing, version control, microservices architecture, building Rest Ful APIs,
• Distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts
• Machine Learning and Deep Learning:
  Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and
• Neural network, NLP, computer vision, and predictive analytics
• Model Performance and Governance: model monitoring, model validation, bias detection, explainability, performance, drift, outliers and agent observability etc.
• Model Deployment:
  Thorough Understanding of ADLC (Agent Development Life Cycle), CI/CD/CT pipelines (using tools like Git Actions, Jenkins, Cloud Bees etc.),
• A/B testing. Pipeline frameworks like MLFlow, Sage Maker pipeline etc. model and data versioning.
• Data Integration, Transformation & Processing:
  Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big
• data ecosystems, relational, NOSQL and graph databases, unstructured and semi-structured data. Data processing on distributed systems with Spark/Py Spark
• Knowledge of how databases are structured and function to use them e]iciently.
• May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc. SQL (relational), Unstructured
• (No
  
  SQL), Graph/ontology (Graph DB), semantic data models.
• Statistics and Computing:
  Strong knowledge of:
  Linear Algebra, Probability and