AI Engineer (Financial Services)

Location: [Onsite / Hybrid / Remote]

Employment Type: Full-Time (W2 Only)

No Corp-to-Corp (C2C)

About The Role

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.

Key Responsibilities

• 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