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New York Technology Partners · 13 hours ago

Senior AI/ML Developer Lead

Clinton, NJ

Contract

Onsite

Senior Level, Lead/Staff

14+ years exp

New York Technology Partners is seeking a Senior AI/ML Developer Lead to drive enterprise AI modernization initiatives. This role combines deep technical expertise, architectural ownership, and team leadership to accelerate delivery and modernize legacy platforms using advanced Machine Learning and Generative AI technologies.

ConsultingInformation TechnologySoftwareStaffing Agency

H1B Sponsor Likely

Responsibilities

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

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

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

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

Evaluate, onboard, and integrate external AI platforms, LLM providers, and vendor solutions

Lead technical PoCs, architecture assessments, and deep dives with third-party partners

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

Qualification

AI/ML experienceGenerative AI solutionsPython programmingDeep Learning & NLPStatistical modelingCloud & DevOpsData & PipelinesModel OpsTeam leadershipCommunication skills

Required

14+ years of overall software engineering experience with progression into senior technical leadership roles

8+ years of hands-on AI/ML experience, including model development, deployment, and production operations

Proven enterprise experience delivering Generative AI solutions, including RAG pipelines and agent-based AI systems, end to end

Core AI / ML skills: 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 skills: Retrieval-Augmented Generation (RAG) architectures, Vector databases (pgvector, Elasticsearch), Agentic AI workflows (Autogen, MCP or similar frameworks), Enterprise chatbots and document intelligence systems

Programming skills: 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

Technical leadership across the full AI/ML solution lifecycle, from design and development to production deployment and optimization

Architecture decisions for ML platforms, GenAI solutions (RAG, agentic workflows), and data pipelines supporting CoreMod modernization