Technical Architect Machine Learning

Quantiphi; Karnataka; On-site; INR 3,500,000 - 7,000,000; Full time

Benefits offered by this job

Health insurance

Flexible work arrangements

Professional development budget

Job summary

Quantiphi is seeking a senior ML Architect to design and implement scalable, production-ready ML solutions on cloud platforms. You will lead model development for time-series forecasting and automated quality scoring, integrating legacy systems with modern ML pipelines.

The role requires deep expertise in Python/SQL, PySpark, and MLOps, with hands-on experience in large-scale data processing, model deployment, and AI governance.

Qualifications

• Advanced degree or equivalent practical experience in ML/AI and data science.
• Proven track record building production-grade ML/AI systems.
• Strong knowledge of modern cloud ML platforms (Azure/GCP/AWS).

Responsibilities

• Design and architect multi-layer ML solutions for production.
• Integrate legacy systems with modern Azure ML platforms.
• Develop and deploy models for curve prediction and quality scoring.
• Lead MLOps practices including monitoring and retraining.

Skills

Python & SQL (PySpark)

Time Series Forecasting

LLM Orchestration

Cloud ML Platforms

MLOps / CI/CD

AI Governance

Containerization (Docker/Kubernetes)

Model Deployment

Education

Master's degree in CS or related field

Tools

Databricks

Azure AI Foundry

LangGraph

Semantic Kernel

XGBoost/LightGBM

PySpark

Job description

• 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.
• 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.