Quantiphi
Website:
quantiphi.com
Company:
https://www.linkedin.com/company/quantiphi
Seniority: Mid-Senior level
Industries: Artificial Intelligence
Job details:
Role & Responsibilities:
- Design and architect multi-layered ML solutions using Azure AI Foundry and ensuring they are robust, scalable, and production-ready.
- Defining and implementing robust integration strategies between legacy control software (e.g., VB6) and modern Azure ML platforms, ensuring seamless data flow, scalability, and strict data security governance.
- Architecting, developing, and deploying advanced machine learning and deep learning models specifically for curve prediction and automated quality scoring.
- Designing and implementing sophisticated optimization engines to automate precision movements with micron-level accuracy.
- Leading the transition from manual operator heuristics and rule-based systems to automated, data-driven logic and decision-making processes.
- Design and architect multi-layered Agentic AI solutions using Azure AI Foundry and LLM Orchestration frameworks (e.g., LangGraph, Semantic Kernel).
- Define the technical roadmap for "Plan-and-Execute" agentic loops, specifically for unstructured data extraction and autonomous system updates.
- Lead model selection and evaluation strategies, comparing high-reasoning models like Claude 3.5 Sonnet with open-weight alternatives (Llama 3.1) for cost and performance optimization.
- Performing extensive data pre-processing on historical calibration data to train and validate models, and building robust production monitoring, alerting, and retraining scripts.
- Continuously enhancing, modifying, optimizing, and maintaining existing ML models to improve performance, accuracy, and efficiency.
- Working closely with external and internal stakeholders to define requirements for ML use cases, gather feedback, and drive solution enhancements.
- Generating actionable insights from large datasets and effectively communicating complex analysis results and model performance to clients and internal stakeholders.
- Provide technical leadership to MLEs and Platform engineers to ensure architectural alignment across workstreams.
Skills Expectation:
- Expertise in Python and SQL with PySpark for large-scale data processing.
- Experience Expertise in Tree-based models (e.g., XGBoost, LightGBM).
- 10+years Strong foundation in Traditional ML algorithms (e.g., Logistic Regression, Cluster Analysis, Decision Trees, Statistical Modeling, Predictive Analysis, Regression, Classification).
- Experience with Encoder-Decoder architectures for sequence modeling.
- Proficiency in Deep Learning Architectures specifically for time series and curve data.
- Deep expertise in Agentic AI, Time Series forecasting, Descriptive Machine Learning, and Exploratory Data Analysis.
- Strong experience with Google Cloud Platform (GCP) Or Azure Cloud Services for ML workloads.
- Expertise in LLM Orchestration patterns (ReAct, Chain-of-Thought, multi-agent collaboration).
- Deep experience with Azure AI services, including AI Search, AI Foundry, and Azure OpenAI.
- Proven ability to design, develop, and deploy production-grade Machine Learning and Generative AI systems that deliver measurable ROI and contribute to long-term AI roadmaps.
- Experience with advanced optimization techniques for black-box functions, including Bayesian Optimization and sophisticated hyperparameter tuning methods.
- Experience with Databricks for ML workflows and data engineering.
- Must have experience in putting models into production.
- Experience in post-production deployment, including MLOps practices (monitoring, retraining, versioning), would be a significant plus.
- Experience working with scalable, highly-interactive, high-performance ML systems and projects.
- Great analytical skills with meticulous attention to detail.
- Strong stakeholder and team management skills.
- Experience with AI Governance, compliance, and risk mitigation strategies.
- Proven experience in deploying and monitoring LLMs in a production environment.
- Familiarity with containerization (Docker, Kubernetes) for ML model serving.
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