AI Engineer (Financial Services)

Location: Onsite / Hybrid / Remote
Employment Type: Full-Time (W2 Only)
No Corp-to-Corp (C2C)

We are seeking an AI Engineer to design develop and deploy enterprise AI solutions within a financial services environment. This role focuses on building production-grade AI systems using machine learning large language models (LLMs) and data-driven architectures to support use cases such as risk analysis fraud detection customer intelligence and regulatory automation.

You will work in a highly regulated environment ensuring all AI solutions are secure explainable and compliant with financial standards.

• Design and develop AI/ML models and LLM-based applications for enterprise use cases
• Build and deploy RAG (Retrieval-Augmented Generation) pipelines and intelligent systems
• Develop scalable data pipelines and AI workflows
• Integrate AI solutions with enterprise systems APIs and data platforms
• Implement model monitoring evaluation and optimization
• Ensure explainability auditability and compliance of AI systems
• Work with large structured and unstructured datasets
• Collaborate with cross-functional teams to deliver AI-driven solutions
• Support deployment using cloud platforms and MLOps practices

• 4 years of experience in AI/ML engineering or data science
• Strong programming skills in Python
• Experience with machine learning frameworks (TensorFlow PyTorch Scikit-learn)
• Experience building LLM-based applications and RAG pipelines
• Strong understanding of data processing feature engineering and model evaluation
• Experience with APIs microservices and system integration
• Experience working in enterprise environments (financial preferred)

• Experience with LLMs (GPT Claude Llama Mistral)
• Familiarity with vector databases (Pinecone FAISS Weaviate)
• Experience with MLOps tools and model deployment
• Experience with cloud platforms (AWS Azure GCP)
• Knowledge of data security governance and compliance requirements
• Exposure to financial use cases (fraud detection risk compliance)

• Strong AI/ML engineering and problem-solving skills
• Ability to build scalable production-ready AI systems
• Understanding of model explainability and regulatory requirements
• Experience delivering enterprise AI solutions in real-world environments