Senior Data Scientist – AI/ML

novasoft; Jersey City (NJ); Hybrid; USD 120,000 - 160,000; Full time

Job summary

A leading company in AI solutions is seeking an experienced AI/ML Data Scientist to architect and maintain advanced machine learning systems. The ideal candidate will utilize their expertise in distributed training and MLOps to scale enterprise AI platforms, working hands-on in cloud environments to deliver business-ready solutions.

Qualifications

• 6-10 years of experience in AI/ML, with expertise in model deployment.
• Strong Python skills with experience in ML libraries.
• Proficient in deep learning frameworks like TensorFlow and PyTorch.

Responsibilities

• Architect and implement distributed training strategies.
• Design and deploy containerized ML models via Docker and Kubernetes.
• Develop robust model monitoring and drift detection systems.

Skills

Python

Deep Learning

Distributed Computing

MLOps

Model Monitoring

Tools

NumPy

TensorFlow

PyTorch

Docker

Kubernetes

Airflow

Apache Spark

Job description

Location:Jersey City/ Remote
Experience:6+ years
Employment Type:Contract

Position Overview

We are seeking an experiencedAI/ML Data Scientistto architect, deploy, and maintain advanced machine learning systems at scale. The ideal candidate is highly proficient in distributed training, MLOps, and ML model serving infrastructure, and will play a key role in shaping and scaling enterprise AI platforms.

This is a hands-on engineering role focused on deep learning, containerized ML deployment, and real-time inference systems in a cloud-based environment.

Key Responsibilities

• Architect and implementdistributed training strategiesusing frameworks likeHorovodandDeepSpeed.
• Design and deploy containerized ML models viaDocker,Kubernetes, and modern serving platforms likeTensorFlow Serving,TorchServe, andSeldon Core.
• Develop robustmodel monitoring,drift detection, and logging systems to ensure reliability in production.
• LeadCI/CD pipeline developmentand enforceMLOps best practicesfor rapid and safe model iteration.
• Optimize inference pipelines forlow-latency model servingacross real-time and batch workloads.
• Integrate models with distributed data storage platforms andvector databasesto support scalable feature retrieval.
• Troubleshoot and debugcomplex distributed AI/ML systemsfor performance and scalability.
• Collaborate across data engineering, DevOps, and product teams to deliver business-ready AI solutions.

Must-Have Qualifications

• 6–10 years of experience in AI/ML, with deep expertise in training and deploying ML models in production.
• Strong Python skills with experience inNumPy,SciPy, and machine learning libraries.
• Proficient indeep learning frameworks:TensorFlow,PyTorch, and associated ecosystems.
• Experience withdata orchestration toolssuch asAirflow,Kubeflow, or similar.
• Hands-on knowledge offeature engineering platforms(e.g.,Feast,Tecton).
• Solid background indistributed computingusingApache Spark,Dask, or similar platforms.
• Strong experience withcontainerizationandorchestrationvia Docker and Kubernetes.
• Familiarity withmodel serving frameworks:TF Serving,TorchServe, orSeldon Core.
• Proven ability to implementmodel monitoringandconcept drift detectionpipelines.
• In-depth understanding ofdata formats and serialization:Parquet,Avro,Protocol Buffers.

Preferred Qualifications

• Prior experience deployingAI/ML platforms in insurance, finance, or healthcareenvironments.
• Knowledge of vector search andreal-time feature storesfor high-performance retrieval.
• Exposure tomulti-cloud or hybrid cloud ML infrastructure.
• Contributions to open-source AI/ML tooling is a plus.

Application Process:
Submit yourupdated resumeand abrief portfolioor summary highlighting your AI/ML deployments, infrastructure contributions, and specific experience with distributed model training and serving.