Partner with Data Scientists, ML Engineers, Data Engineers, application teams, and business stakeholders to translate business use cases into scalable AI solutions.
Define reusable AI architecture patterns, frameworks, and components that can be applied across multiple enterprise use cases.
Establish patterns for prompt management, model abstraction, model routing, model versioning, and evaluation.
Design architectures for model inference, feature pipelines, model serving, and real-time or batch scoring.
Implement and guide development of Python-based AI services, APIs, pipelines, and orchestration components.
Ensure AI solutions meet requirements for security, privacy, scalability, reliability, performance, explainability, governance, and auditability.
Evaluate open-source and commercial AI frameworks and determine appropriate technologies based on enterprise requirements.
Define technical approaches for model monitoring, hallucination detection, evaluation, guardrails, observability, and human-in-the-loop controls.
Lead architecture and code reviews and provide technical guidance through implementation and production rollout.
Required Experience
Strong experience as an AI Architect / ML Architect / GenAI Architect / Lead AI Engineer within large-scale enterprise environments.
Deep hands-on programming experience with Python.
Strong understanding of Machine Learning and Deep Learning fundamentals.
Hands-on experience with frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent.
Strong experience building LLM-based applications and GenAI solutions.
Experience working with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks.
Strong experience with RAG architectures, embeddings, vector search, semantic retrieval, and reranking.
Experience designing and building agentic AI solutions with tool calling, workflow orchestration, memory, and multi-step reasoning.
Strong understanding of prompt engineering, prompt versioning, prompt evaluation, and structured outputs.
Experience building REST APIs and backend AI services using frameworks such as FastAPI, Flask, or equivalent.
Strong understanding of model lifecycle concepts including training, fine-tuning, inference, evaluation, monitoring, and versioning.
Experience integrating AI solutions with enterprise data sources, APIs, databases, messaging systems, and application platforms.
Understanding of data pipelines, feature engineering, data quality, and model input validation.
Experience building scalable and production-ready AI systems rather than only proof-of-concepts.
GenAI / LLM Expertise
Experience designing solutions using large language models and foundation models.
Strong understanding of
Prompt engineering
RAG architectures
Embeddings and vector retrieval
Tool/function calling
Agentic workflows
Structured outputs
Context management
Model routing
Guardrails
Evaluation frameworks
Hallucination mitigation
Ability to evaluate when to use RAG, fine-tuning, traditional ML, rules-based logic, or agentic approaches depending on the business problem.
Experience designing enterprise AI systems that can work across multiple models without creating tight vendor dependency.
Machine Learning Expertise
Strong understanding of supervised and unsupervised learning, classification, regression, clustering, anomaly detection, and recommendation approaches.
Experience with feature engineering, feature selection, model training, model validation, and inference.
Understanding of ML evaluation metrics and model performance analysis.
Experience integrating ML models into enterprise applications and operational workflows.
Familiarity with ML pipelines, model registries, experiment tracking, and model monitoring.
AI Governance & Production Readiness
Define controls for model and prompt versioning, evaluation, observability, explainability, and traceability.
Design AI systems with appropriate security, privacy, data protection, and access controls.
Establish approaches for human review and escalation where AI outputs require oversight.
Support implementation of automated evaluation and testing across GenAI and ML solutions.
Ensure solutions can be monitored for quality degradation, model drift, hallucinations, latency, and reliability.
Preferred Experience
Experience within Banking, Payments, Fraud, Cards, Financial Crime, Wealth, or other financial-services domains.
Experience developing AI solutions using customer, transaction, payment, behavioral, or risk data.
Experience building AI-enabled decisioning, fraud detection, investigation, or operational automation solutions.
Experience working in complex on-premise or hybrid enterprise environments.
Familiarity with distributed data technologies such as Spark and Kafka.
Experience with enterprise AI platforms, model gateways, or centralized AI orchestration platforms.
Key Profile We Are Looking For
A hands-on AI Architect who can architect, code, prototype, and guide implementation.
The individual should be comfortable moving across
Business Use Case → Data → ML / LLM → RAG / Agents → Python Services → APIs → Enterprise Integration → Evaluation → Production
The ideal candidate should be able to write Python, build a working AI prototype, troubleshoot model or RAG behavior, design the enterprise architecture, and guide engineering teams to productionize the solution.
Compensation, Benefits and Duration
Minimum Compensation: USD 62,000
Maximum Compensation: USD 217,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post.
