Location:- Newark NJ
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Role: Lead ML Engineer
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Job description:
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As a Lead, Machine Learning Engineer, you will be leading the engineering of Agentic AI
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systems and partner with Data Scientists, Data Engineers, Data Analysts, DevSecOps and
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other professionals to implement machine learning models and Agentic AI systems that
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will deliver stability, producibility, scalability and integration with other products and
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services. You will implement capabilities to solve sophisticated business problems,
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deploy innovative products, services and experiences to delight our customers! In addition
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to advanced technical expertise and experience, you will bring excellent problem solving,
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communication and teamwork skills, along with agile ways of working, strong business
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insight, an inclusive leadership attitude and a continuous learning focus to all that you do.
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Key Responsibilities
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- Architect, Design and Development and Deployment of ML models and Agentic AI systems that solve real-world business problems, while working in collaboration
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with the Product and Data Science teams; remove complex technical impediments
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- Solve complex problems by writing and testing application code, developing and validating ML models and Agentic AI, and automating tests and deployment using
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LLM tools like Claude Code.
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- Leverage cloud-based architectures and technologies to deliver optimized ML models at scale
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- Construct optimized data, ingestion and memory pipelines to feed ML models and Agentic systems
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- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML
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models and application code
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- 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
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- Work on complex problems in which analysis of situations or data requires and evaluation of intangible variables.
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- Use programming languages including but not limited to Python, C++, SQL
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The Skills and expertise you bring:
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- Bachelor of Computer Science or Engineering or experience in related fields
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- Ability to coach others with minimal guidance and effectively leverage diverse
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ideas, experiences, thoughts and perspectives to the benefit of the organization
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- Experience with agile development methodologies and Specification-Driven Development (SDD)
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- Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
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- Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges
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- Excellent problem solving, communication and collaboration skills
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- Advanced experience and/or expertise with several of the following:
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- Software Engineering & System Design: Requirement analysis, coding, and testing, version control, microservices architecture, building RestFul APIs,
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- Distributed computing, architecture patterns, general understanding of computer architecture, Object-oriented programming concepts
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- Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and
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reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc.,
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- Neural network, NLP, computer vision, and predictive analytics
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- Model Performance and Governance: model monitoring, model validation, bias detection, explainability, performance, drift, outliers and agent observability etc.
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- Model Deployment: Thorough Understanding of ADLC (Agent Development Life Cycle), CI/CD/CT pipelines (using tools like GitActions, Jenkins, CloudBees etc.),
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- A/B testing. Pipeline frameworks like MLFlow, SageMaker pipeline etc. model and data versioning.
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- Data Integration, Transformation & Processing: Transforming and mapping raw data to generate insights. Data wrangling through various tools. Understanding big
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- data ecosystems, relational, NOSQL and graph databases, unstructured and semistructured data. Data processing on distributed systems with Spark/PySpark
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- Knowledge of how databases are structured and function to use them e]iciently.
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- May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc. SQL (relational), Unstructured
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- (NoSQL), Graph/ontology (Graph DB), semantic data models.
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- Statistics and Computing: Strong knowledge of: Linear Algebra, Probability and
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- Statistics, Multivariate Calculus, Distributions like Poisson, Normal, Binomial etc.
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- Programming Languages: Python, C++, SQL
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