AI Architect - GenAI / ML

Job Description

AI Architect – GenAI / ML

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Role Overview

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Photon is looking for a hands-on AI Architect – GenAI / ML to support large-scale AI-led transformation initiatives within the financial services industry.

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This role will be responsible for defining and implementing solution architectures across Generative AI, Machine Learning, agentic AI, enterprise data, APIs, and application platforms .

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The ideal candidate will combine strong AI architecture experience with deep hands-on expertise in Python, ML frameworks, LLM-based applications, orchestration frameworks, and production AI engineering .

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Key Responsibilities

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• Define the end-to-end architecture for GenAI and ML solutions , from data ingestion and feature engineering through model execution, orchestration, integration, and production deployment.
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• Design enterprise-grade solutions leveraging LLMs, traditional ML models, retrieval-augmented generation, agents, and AI orchestration frameworks .
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• Build and validate prototypes and reference implementations using Python .
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• Architect RAG solutions , including document ingestion, chunking, embeddings, retrieval, reranking, prompt construction, and response generation.
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• Design agentic AI architectures supporting tool usage, workflow orchestration, reasoning, memory, and multi-agent interactions.
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• Define patterns for integrating AI capabilities with enterprise applications, APIs, data platforms, event streams, and legacy systems .
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• Partner with Data Scientists, ML Engineers, Data Engineers, application teams, and business stakeholders to translate business use cases into scalable AI solutions.
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• Define reusable AI architecture patterns, frameworks, and components that can be applied across multiple enterprise use cases.
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• Establish patterns for prompt management, model abstraction, model routing, model versioning, and evaluation .
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• Design architectures for model inference, feature pipelines, model serving, and real-time or batch scoring.
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• Implement and guide development of Python-based AI services, APIs, pipelines, and orchestration components .
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• Ensure AI solutions meet requirements for security, privacy, scalability, reliability, performance, explainability, governance, and auditability .
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• Evaluate open-source and commercial AI frameworks and determine appropriate technologies based on enterprise requirements.
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• Define technical approaches for model monitoring, hallucination detection, evaluation, guardrails, observability, and human-in-the-loop controls .
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• Lead architecture and code reviews and provide technical guidance through implementation and production rollout.
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Required Experience

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• Strong experience as an AI Architect / ML Architect / GenAI Architect / Lead AI Engineer within large-scale enterprise environments.
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• Deep hands-on programming experience with Python .
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• Strong understanding of Machine Learning and Deep Learning fundamentals .
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• Hands-on experience with frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent .
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• Strong experience building LLM-based applications and GenAI solutions .
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• Experience working with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks .
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• Strong experience with RAG architectures, embeddings, vector search, semantic retrieval, and reranking .
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• Experience designing and building agentic AI solutions with tool calling, workflow orchestration, memory, and multi-step reasoning.
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• Strong understanding of prompt engineering, prompt versioning, prompt evaluation, and structured outputs .
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• Experience building REST APIs and backend AI services using frameworks such as FastAPI, Flask, or equivalent .
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• Strong understanding of model lifecycle concepts including training, fine-tuning, inference, evaluation, monitoring, and versioning .
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• Experience integrating AI solutions with enterprise data sources, APIs, databases, messaging systems, and application platforms .
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• Understanding of data pipelines, feature engineering, data quality, and model input validation.
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• Experience building scalable and production-ready AI systems rather than only proof-of-concepts.
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GenAI / LLM Expertise

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• Experience designing solutions using large language models and foundation models .
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• Strong understanding of:
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• Prompt engineering
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• RAG architectures
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• Embeddings and vector retrieval
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• Tool/function calling
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• Agentic workflows
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• Structured outputs
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• Context management
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• Model routing
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• Guardrails
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• Evaluation frameworks
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• Hallucination mitigation
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• Ability to evaluate when to use RAG, fine-tuning, traditional ML, rules-based logic, or agentic approaches depending on the business problem.
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• Experience designing enterprise AI systems that can work across multiple models without creating tight vendor dependency.
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Machine Learning Expertise

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• Strong understanding of supervised and unsupervised learning, classification, regression, clustering, anomaly detection, and recommendation approaches .
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• Experience with feature engineering, feature selection, model training, model validation, and inference.
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• Understanding of ML evaluation metrics and model performance analysis.
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• Experience integrating ML models into enterprise applications and operational workflows.
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• Familiarity with ML pipelines, model registries, experiment tracking, and model monitoring.
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AI Governance & Production Readiness

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• Define controls for model and prompt versioning, evaluation, observability, explainability, and traceability .
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• Design AI systems with appropriate security, privacy, data protection, and access controls .
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• Establish approaches for human review and escalation where AI outputs require oversight.
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• Support implementation of automated evaluation and testing across GenAI and ML solutions.
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• Ensure solutions can be monitored for quality degradation, model drift, hallucinations, latency, and reliability .
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Preferred Experience

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• Experience within Banking, Payments, Fraud, Cards, Financial Crime, Wealth, or other financial-services domains .
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• Experience developing AI solutions using customer, transaction, payment, behavioral, or risk data .
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• Experience building AI-enabled decisioning, fraud detection, investigation, or operational automation solutions.
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• Experience working in complex on-premise or hybrid enterprise environments .
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• Familiarity with distributed data technologies such as Spark and Kafka .
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• Experience with enterprise AI platforms, model gateways, or centralized AI orchestration platforms.
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Key Profile We Are Looking For

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A hands-on AI Architect who can architect, code, prototype, and guide implementation .

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The individual should be comfortable moving across:

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Business Use Case → Data → ML / LLM → RAG / Agents → Python Services → APIs → Enterprise Integration → Evaluation → Production

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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 .