Senior software engineer jobs in Lawrence, NJ

We are seeking a senior AI/ML Developer Lead with 14+ years of overall software development experience to drive enterprise AI modernization initiatives under the CoreMod program. This role is hands-on and strategic, combining deep technical expertise, architectural ownership, and team leadership to accelerate delivery, close SME gaps, and modernize legacy platforms using advanced Machine Learning and Generative AI technologies. The ideal candidate brings a strong foundation in full-stack and backend development, evolved into AI/ML and GenAI leadership, with a proven ability to design, build, and scale production-grade AI systems in regulated enterprise environments. What You'll Be Doing: AI/ML & Engineering Leadership Provide technical leadership across the full AI/ML solution lifecycle, from design and development to production deployment and optimization. Own architecture decisions for ML platforms, GenAI solutions (RAG, agentic workflows), and data pipelines supporting CoreMod modernization. Act as the go-to SME for AI/ML, guiding design standards, best practices, and delivery quality across teams. Mentor and review work of ML engineers and developers, ensuring high code quality, scalability, and reliability. Enterprise AI Modernization Lead modernization of legacy decisioning and analytics systems by introducing cloud-native ML architectures and microservices. Design and deliver batch and real-time ML systems, including forecasting engines, risk models, and intelligent automation. Drive adoption of Generative AI for document intelligence, enterprise search, chatbots, and workflow automation. Ensure solutions meet security, compliance, and governance standards required in enterprise and financial domains. Hands-on Development & Architecture Contribute directly to development using Python and backend frameworks such as FastAPI, building scalable ML APIs and services. Build and deploy models using PyTorch, TensorFlow, Scikit-learn, LightGBM, XGBoost, and transformer-based LLM frameworks. Design feature engineering and data pipelines using Spark, Snowflake, Databricks, Airflow, and cloud-native services. Implement containerized deployments using Docker, Kubernetes, Helm, and manage CI/CD pipelines with Jenkins and GitHub Actions. Establish model lifecycle practices using MLflow, Kubeflow, SageMaker, or equivalent tooling. Cross-Functional & Stakeholder Collaboration Partner with product, data, platform, and business teams to translate complex business problems into scalable AI/ML solutions. Lead technical discussions, design reviews, and roadmap planning with internal and external stakeholders. Support agile delivery through technical backlog grooming, sprint planning, and estimation. Vendor & Platform Enablement Evaluate, onboard, and integrate external AI platforms, LLM providers, and vendor solutions. Lead technical PoCs, architecture assessments, and deep dives with third-party partners. Communication & Influence Clearly articulate AI/ML architectures, trade-offs, and outcomes to executive, technical, and non-technical audiences. Produce technical documentation, architecture diagrams, and delivery artifacts. Provide confident, polished communication that builds trust and alignment across the organization. Required Experience: 14+ years of overall software development experience, with progressive responsibility into technical leadership roles. 8+ years of hands-on AI/ML experience, including model development, deployment, and production operations. Proven experience leading enterprise-scale AI/ML or GenAI initiatives end to end. Strong background in financial services, banking, or regulated environments preferred. Key Technical Skills: Core AI / ML Statistical modeling, hypothesis testing, regression, classification, clustering, time series forecasting, A/B testing Models: Random Forest, Gradient Boosting (XGBoost, LightGBM), SVM, Neural Networks (CNN, RNN, LSTM) Deep Learning & NLP: Transformers, BERT-based models, document parsing, semantic search Generative AI Retrieval-Augmented Generation (RAG) architectures Vector databases (pgvector, Elasticsearch) Agentic AI workflows (Autogen, MCP or similar frameworks) Enterprise chatbots and document intelligence systems Engineering & Platforms Programming: Python, Java, SQL Backend & APIs: FastAPI, Flask, Microservices Architecture Data & Pipelines: Spark, Snowflake, Databricks, Airflow Cloud & DevOps: AWS / GCP, Docker, Kubernetes, Helm, CI/CD (Jenkins, GitHub Actions) Model Ops: MLflow, Kubeflow, SageMaker Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Statistics, or related field. Thanks!