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Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
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The AI/ML Engineer (GenAI), G&A Solutions Engineering (GSE) role involves joining the iRecon Payments team to build a next-generation payments platform. You will act as a technical catalyst, modernizing complex product architectures to enable full observability and autonomous workflows for reconciliation, invoicing, and payments. The core work involves blending classical machine learning with generative and agentic AI to transform how transactional data is processed at scale within the financial domain. Key responsibilities include navigating the intersection of financial data and large language models to drive operational efficiency. Required skills include experience with supervised/unsupervised learning, transformer architecture, LLMs, and Agentic AI concepts. You will utilize tools like LangChain, LlamaIndex, AutoGen, LangGraph, CrewAI, and techniques such as PEFT, LoRA, RAG, and RLHF to integrate advanced AI into existing systems.
Modernize complex product architectures to enable full observability and autonomous workflows for reconciliation, invoicing, and payments.
Integrate Large Language Models with financial data to drive productivity and operational efficiency.
Build and extend RAG, MCP, or multi-agent frameworks using tools like LangChain, LlamaIndex, or AutoGen.
Fine-tune Large Language Models using PEFT/LoRA for domain-specific tasks.
Develop conversational assistants and autonomous agents using advanced techniques like LangGraph, CrewAI, and Chain of Thought.
Implement the full LLM lifecycle including pre-training, supervised fine-tuning, and reinforcement learning.
Integrate emerging AI tools into existing legacy technology stacks.
What we're looking for
Bachelor's degree in Computer Science, AI, Machine Learning, or relevant work experience.
2+ years of experience building machine learning solutions using supervised/unsupervised learning, classification, recommendation systems, and clustering algorithms.
In-depth knowledge of transformer architecture, LLMs, and Agentic AI concepts.
Hands-on experience fine-tuning Large Language Models (LLMs) using PEFT/LoRA for domain-specific tasks.
Proven experience building and extending RAG, MCP, or multi-agent frameworks like LangChain, LlamaIndex, or AutoGen.
3+ years of experience deploying production-grade AI/ML solutions in the FinTech domain.
2+ years of experience building conversational assistants or autonomous agents using advanced techniques like LangGraph, CrewAI, and Chain of Thought.
Experience with the full LLM lifecycle including pre-training, SFT, and Reinforcement Learning techniques (RLHF, PPO, GRPO).
Artificial Intelligence
Machine Learning
Python
Big Data
Statistical Techniques
Data Transformation
Model Selection
Cross-Validation
System Architecture
Systems Modeling
Simulation
Data Engineering
AIOps
ML Ops
model ops
data ops
Python
Linux Shell Script
Kubernetes
Docker
Jenkins
CI/CD
DevSecOps
AWS
Azure
Google Cloud Platform
Container Orchestration
Zero Trust
Machine Learning Frameworks