AI/ML & Generative AI Engineer

Surge Technology Solutions Inc; Jersey City (NJ); On-site; USD 150,000 - 210,000; Full time

Job summary

Surge Technology Solutions Inc in Jersey City seeks an experienced AI/ML & Generative AI Engineer to design and implement scalable AI solutions for banking and financial services.

The role involves end‑to‑end AI/ML pipelines, NLP, time‑series forecasting, LLM apps, RAG, MLOps, and deployment on AWS/Azure with Docker and Kubernetes.

Qualifications

• 5+ years in AI/ML, Data Science, or related field
• Experience in banking/financial services domain
• Proficient in Python and SQL
• Hands-on with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and NLP tools
• Experience with end-to-end ML pipelines, MLOps, and deployment

Responsibilities

• Design and develop AI/ML solutions using supervised/unsupervised learning and NLP
• Build Generative AI apps with LLMs, RAG, and fine-tuning
• Apply AI/ML to banking/financial services problems
• Develop end-to-end ML pipelines: ingestion, training, deployment, monitoring
• Containerize AI apps with Docker and deploy on Kubernetes
• Expose models via REST APIs and microservices
• Leverage AWS/Azure services (SageMaker/Azure ML) for workloads
• Collaborate with stakeholders to deliver scalable AI solutions

Skills

Python

SQL

Generative AI & LLMs

MLOps

Docker & Kubernetes

Cloud platforms (AWS/Azure)

Spark/Databricks

Data engineering

Education

Bachelor's or Master's in CS/AI/Engineering

Tools

Docker

Kubernetes

Spark

Databricks

Microsoft Fabric

Amazon SageMaker

Azure Machine Learning

Job description

Employment Type: W2 or 1099........ (No C2C)

Visa:H4EAD ,Green Card, US Citizens (Only USA Applicants)

Job Summary

We are looking for an experienced AI/ML & Generative AI Engineer with strong expertise in machine learning, generative AI, MLOps, and cloud technologies to design and implement scalable AI solutions for the banking and financial services domain.

The ideal candidate will have hands‑on experience building end‑to‑end AI/ML solutions, including traditional machine learning, deep learning, NLP, time‑series forecasting, anomaly detection, LLM applications, RAG, AI agents, and MLOps. The candidate should be proficient in Python and SQL and experienced with modern AI/ML frameworks, cloud platforms, data engineering ecosystems, and enterprise application integration.

Key Responsibilities

• Design and develop AI/ML solutions using supervised and unsupervised learning, deep learning, NLP, time‑series forecasting, and anomaly detection techniques.


• Build Generative AI applications using LLMs, prompt engineering, fine‑tuning, RAG, and AI agent frameworks.


• Apply AI/ML technologies to solve complex banking and financial services business problems.


• Develop and maintain end‑to‑end AI/ML pipelines, including data ingestion, preprocessing, model training, validation, deployment, monitoring, and continuous improvement.


• Build scalable AI solutions using Python and SQL and modern machine learning libraries.


• Work with large‑scale data ecosystems including ETL pipelines, data lakes, data warehouses, and streaming platforms.


• Leverage technologies such as Apache Spark, Databricks, and Microsoft Fabric for large‑scale data processing and analytics.


• Implement MLOps best practices, including CI/CD, model versioning, governance, explainability, monitoring, and automated model deployment.


• Containerize AI/ML applications using Docker and deploy workloads using Kubernetes.


• Develop and expose AI/ML models through REST APIs and microservices for integration with enterprise applications.


• Deploy and manage AI/ML workloads using AWS and/or Azure, including platforms such as Amazon SageMaker and Azure Machine Learning.


• Collaborate with business, data, engineering, architecture, security, and technology stakeholders to translate requirements into scalable AI solutions.


• Measure and communicate AI solution ROI, business value, performance, and value realization.


• Develop reusable AI frameworks, accelerators, and components to improve development efficiency.


• Build AI copilots and intelligent applications using LangChain, Semantic Kernel, and AI agent orchestration frameworks.


• Contribute to semantic models, knowledge graphs, and enterprise AI solutions.


• Explore and implement synthetic data generation techniques where appropriate.


• Establish and promote AI engineering, MLOps, governance, security, and responsible AI best practices.

Required Skills & Experience

• 5+ years of experience in AI/ML, Data Science, Machine Learning Engineering, or a related field.


• Strong experience working in the banking or financial services domain.


• Strong programming skills in Python and SQL.


• Hands‑on experience with Scikit‑learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and/or NLTK.


• Strong understanding of machine learning algorithms and statistical modeling.


• Experience with supervised/unsupervised learning, deep learning, NLP, forecasting, and anomaly detection.


• Hands‑on experience developing Generative AI and LLM‑based applications.


• Experience with prompt engineering, RAG, LLM fine‑tuning, embeddings, vector databases, and AI agents.


• Experience developing and managing end‑to‑end ML pipelines.


• Strong understanding of MLOps, model governance, model monitoring, explainability, and CI/CD.


• Experience with Docker and Kubernetes.


• Experience working with Spark, Databricks, Microsoft Fabric, or similar large‑scale data platforms.


• Hands‑on experience with AWS and/or Azure AI/ML services.


• Experience with Amazon SageMaker and/or Azure Machine Learning is highly desirable.


• Experience building APIs, microservices, and enterprise AI integrations.


• Strong understanding of data engineering concepts, including ETL, data lakes, data warehouses, and streaming data.

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Statistics, Mathematics, or a related field.