Lead ML Engineer - Newark NJ

• Location:- Newark NJ
• Role: Lead ML Engineer

Job description:

• 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, DevSecOps 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.
• Key Responsibilities
• 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
• LLM tools like Claude Code.
• 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
• The Skills and expertise you bring:
• 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 RestFul 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
• reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc.,
• 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 GitActions, Jenkins, CloudBees etc.),
• A/B testing. Pipeline frameworks like MLFlow, SageMaker 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 semistructured data. Data processing on distributed systems with Spark/PySpark
• 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
• (NoSQL), Graph/ontology (Graph DB), semantic data models.
• Statistics and Computing: Strong knowledge of: Linear Algebra, Probability and
• Statistics, Multivariate Calculus, Distributions like Poisson, Normal, Binomial etc.
• Programming Languages: Python, C++, SQL