Senior ML/AI Engineer_Hybrid (NYC)

PulseRise Technologies; New York (NY); Hybrid; USD 120,000 - 160,000; Full time

Job summary

PulseRise Technologies is seeking a Senior ML/AI Engineer to build and deploy intelligent systems in an applied AI and data analytics platform. This role requires a strong background in machine learning, a passion for hands-on implementation, and experience with production systems that reason and forecast autonomously.

The position offers a hybrid work model, connecting diverse data sources into a coherent intelligence layer, with responsibilities spanning from prototype to production level.

Qualifications

• 5+ years of experience in applied machine learning and AI, with models deployed in production.
• Deep proficiency in Python with experience in ML frameworks.
• Strong background in statistical analysis and predictive modeling.

Responsibilities

• Design, build, and deploy ML models for demand forecasting and sentiment analysis.
• Develop and iterate on the agentic AI architecture.
• Build and maintain robust ML pipelines from data preprocessing to production.

Skills

Applied machine learning

Artificial Intelligence

Python

Predictive modeling

Statistical analysis

NLP

Large-scale datasets

Education

M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or related field

Tools

PyTorch

TensorFlow

scikit-learn

Graph databases

Job description

We are looking for a Senior ML/AI Engineer to build and deploy the intelligent systems at the core of an applied AI and data analytics platform. This is not research for the sake of papers — it's production systems that reason, forecast, and act autonomously across complex enterprise data landscapes. You will develop the models and agentic architectures that power demand forecasting, consumer intelligence, competitive analysis, and autonomous decision‑making. The same bar as every role on this team: senior enough to think deeply, but still energized by hands‑on implementation. High agency, low ego, great communicator.

Details

Schedule: Full-time
Location: Hybrid (NYC)
Type of collaboration: Full-time employment

The platform connects an organization's entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks. At its core is a production graph RAG system connecting temporal and sentiment data at enterprise scale — a key technical differentiator. You will work at the intersection of applied ML, agentic AI, and graph‑based reasoning. The company runs experiments at the fringes of modern technology — ML, graph databases, agentic AI — and wants engineers who share the drive to stay at the frontier and turn innovation into real product value. This role spans prototype to production, and everything in between.

You have

• 5+ years of experience in applied machine learning and AI, with models deployed and running in production
• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field — or equivalent practical experience (what you've built matters more than the degree)
• Deep proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Strong background in statistical analysis, predictive modeling, and time series forecasting
• Experience with applied agentic AI/ML systems and multi‑agent orchestration
• Experience with NLP, LLMs, and RAG architectures
• Comfort working with large‑scale datasets and distributed computing environments

Nice to have

• Graph database or graph RAG experience (a major plus — core to the stack)
• Background in retail, supply chain, or demand forecasting domains
• Experience with graph neural networks or knowledge graphs
• Familiarity with MLOps platforms and model serving infrastructure
• Contributions to open‑source ML/AI projects or published research

What to do

• Design, build, and deploy ML models for demand forecasting, time series prediction, consumer sentiment analysis, and anomaly detection at enterprise scale
• Develop and iterate on the agentic AI architecture — building systems that reason across heterogeneous data sources and take autonomous action
• Build and maintain robust ML pipelines: data preprocessing, feature engineering, model training, evaluation, and production deployment
• Architect and improve the production graph RAG system
• Build RAG systems and LLM integrations that power natural language interfaces and autonomous workflows
• Collaborate with backend engineers to ensure models are production‑grade — optimized for latency, reliability, and scale
• Own model performance end‑to‑end: monitoring, retraining, and continuous improvement in production
• Stay at the frontier of AI research and bring relevant innovations into the platform