Job Specifications
Role: AI/ML Architect
Location : Alpharetta, GA / Dallas, TX / Chicago, IL /Berkeley Heights, NJ / New York, NY (Can be anywhere in USA as well if they are willing to travel)
Level: Senior (10-12+ years' experience)
Position : 4
Job Description:
Agentic AI, ML, MLOps, Python, Solution Design, LLM’s, AI Proof of concepts, Cloud, DevOps, CI/CD tools
About the Role:
We are seeking a highly experienced AI / ML Engineer to design, develop, and deploy machine‑learning solutions that power key analytics and intelligent automation across our fintech ecosystem. This role focuses on delivering production‑grade ML systems—spanning classical ML, deep learning, and LLM‑based applications—while ensuring scalability, reliability, and regulatory compliance. The engineer will own end‑to‑end model development, from data preparation through deployment and monitoring, and will work closely with engineering, product, and data teams to implement impactful AI capabilities.
Key Responsibilities:
Build, train, and evaluate ML and deep learning models for classification, prediction, anomaly detection, and NLP use cases.
Implement scalable ML pipelines for data processing, feature engineering, and inference.
Develop and integrate LLM‑based capabilities including embeddings, RAG workflows, and fine‑tuned models.
Deploy models to production using containerized and cloud‑native infrastructures (Docker, Kubernetes, Azure/AWS).
Implement MLOps practices including CI/CD integration, experiment tracking, model registries, and monitoring.
Ensure high‑quality data pipelines and automate preprocessing for structured and unstructured data.
Apply model explainability (SHAP, LIME) and Responsible AI principles to ensure transparency and safe use.
Required Qualifications:
Deep hands‑on experience building and deploying ML models (supervised, unsupervised, deep learning, NLP).
Strong Python skills (NumPy, Pandas, Scikit‑learn, PyTorch or TensorFlow).
Experience with SQL and data‑engineering workflows (Spark or Airflow).
Practical experience with LLMs, vector databases, embeddings, and RAG systems.
Familiarity with production ML deployment using Docker, Kubernetes, CI/CD pipelines, and GPU acceleration.
Experience with cloud AI tooling (Azure, AWS, or GCP) and scalable inference pipelines.
Strong understanding of model monitoring, drift detection, and retraining strategies.
Ability to create clear model explanations and apply interpretability tools.
Nice to Have:
Experience in fintech or other regulated industries with compliance requirements (SOC 2, PCI-DSS, FedRAMP)
Background with LangChain, multi‑agent frameworks, or advanced vector search architectures.
Experience implementing governance, safety guardrails, or Responsible AI frameworks.