- Company Name
- Synergy Technologies
- Job Title
- Need AI/ML Architect with Agentic AI, MLOps & Python Experience :: Onsite position:: Locations (GA, TX, IL, NJ, NY)
- Job Description
-
Job Title: AI/ML Architect
Role Summary: Senior AI/ML Architect with 10‑12+ years of experience designing, developing, and deploying production‑grade machine learning solutions across fintech and regulated environments. Leads end‑to‑end model lifecycle, from data preparation to scalable deployment, while ensuring compliance, explainability, and robust MLOps practices.
Expectations:
- Deliver high‑impact AI solutions that integrate classical ML, deep learning, and large language model (LLM) capabilities.
- Own the full model ownership cycle: data engineering, feature extraction, model training, evaluation, deployment, monitoring, and retraining.
- Work cross‑functionally with engineering, product, data, and compliance teams to meet business and regulatory objectives.
- Demonstrate leadership in cloud‑native MLOps, container orchestration, and CI/CD automation.
- Champion Responsible AI principles, model explainability, and safety guardrails.
Key Responsibilities:
1. Build, train, and evaluate ML and deep learning models for classification, prediction, anomaly detection, and NLP.
2. Implement scalable pipelines: data ingestion, feature engineering, vector embeddings, and inference workflows.
3. Develop and integrate LLM‑based capabilities (embeddings, retrieval‑augmented generation, fine‑tuning).
4. Deploy models with Docker, Kubernetes, and cloud services (Azure, AWS, GCP).
5. Establish MLOps pipelines: CI/CD, experiment tracking, model registry, drift detection, and automated retraining.
6. Create reproducible, versioned data pipelines using Spark or Airflow.
7. Apply explainability (SHAP, LIME) and Responsible AI guidelines to model outputs.
8. Ensure compliance with SOC 2, PCI‑DSS, and other relevant regulatory frameworks.
Required Skills:
- Deep expertise in supervised/unsupervised ML, deep learning, and NLP.
- Advanced Python (NumPy, Pandas, scikit‑learn, PyTorch or TensorFlow).
- Proficiency in SQL, Spark, Airflow, and data‑engineering workflows.
- Hands‑on with LLMs, vector databases, embeddings, and RAG systems.
- Experience deploying models with Docker, Kubernetes, GPU acceleration, and CI/CD pipelines.
- Cloud AI tooling: Azure ML, Amazon SageMaker, or GCP AI Platform.
- Model monitoring, drift detection, and retraining strategies.
- Familiarity with model explainability (SHAP, LIME) and Responsible AI practices.
Required Education & Certifications:
- Bachelor’s (or Master’s) degree in Computer Science, Data Science, Machine Learning, or related field.
- 10‑12+ years of professional experience in AI/ML engineering.
- Relevant certifications (e.g., AWS Certified Machine Learning, Azure AI Engineer Associate, Google Cloud Professional Machine Learning Engineer) are preferred although not mandatory.