Interview Type: Webcam only
Description of Duties
Design, implement, and optimize Machine Learning based computer vision components, including:
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Object detection and image classification models.
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OCR pipelines integrating third-party and in-house engines.
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Image quality assessment classifiers (e.g., overexposed, underexposed, obstructed
Develop robust, production-ready Python code with strong Object Oriented Design (OOD) principles.
Collaborate with system archit
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Key Responsibilities:Architect and maintain high-performance data pipelines across diverse data sources.Build scalable data processing solutions on Snowflake, AWS, and GCP.Implement ML algorithms for data quality monitoring and anomaly detection.Develop data models, enforce data governance, and maintain documentation.Collaborate with analysts, data scientists, and stakeholders to deliver actionable insights.Required Skills:14+ years in ETL/ELT, data warehousing, and data modeling.Advanced SQL an
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Job Description:
Design and implement machine learning models and pipelines for real-world applications.Build and maintain robust ML pipelines using tools like Vertex AI Pipelines and Terraform for CI/CD templates and configuration management for ML workflows. Implement onboarding and monitoring processes for deployed ML models. Integrate and manage knowledge graphs and vector databases to support semantic search and retrieval-augmented generation (RAG) systems.Collaborate with cross-functi
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Description:
Tachyon Predictive AI team seeking a hybrid Data Science & ML Ops Engineer to drive the full lifecycle of machine learning solutions—from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.
Key Responsibilities
Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational effic
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Job Summary:
We are looking for a highly skilled Ops Engineer with strong expertise in Terraform, Databricks, MLOps, and Large Language Models (LLMs This role is ideal for someone passionate about automating infrastructure, optimizing data and machine learning workflows, and supporting the deployment of advanced AI models in production environments. You will work closely with data scientists, ML engineers, and DevOps teams to enable scalable, secure, and reliable ML and LLM infrastructure.
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Python Programming: At least 5 years of hands-on experience with Python, particularly inframeworks like FastAPI, Django, Flask, and experience using AI frameworks.Access Control Expertise: Strong understanding of access control models such as Role-BasedAccess Control (RBAC) and Attribute-Based Access Control (ABAC), lang Chain.API and Connector Development: Experience in developing API connectors using Python forextracting and managing access control data from platforms like Azure, SharePoint, J
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Must have-
8+ years of professional ML engineering experience at an AI/ML-focused organization.
Familiarity with the state-of-the-art in behavior learning, language, and/or computer vision.
Experience training large-scale foundation models (VLMs, text-to-video models, etc) utilizing distributed training and high-performance optimization techniques such as quantization, mixed precision, model parallelism, data parallelism or FSDP.
Extensive practical experience with PyTorch.
Strong
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equired Skills and Experience:
Minimum 8 years of hands-on experience in machine learning, artificial intelligence, or data science roles.
Demonstrated experience in the banking or finance sector, with a strong understanding of regulatory compliance.
Advanced programming proficiency in Python, R, or Scala.
Expertise in leading ML libraries and frameworks: TensorFlow, PyTorch, Scikit-learn.
Experience working with big data technologies such as Hadoop and Spark.
Solid knowledge of SQ
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Responsibilities:
Design, develop, and deploy machine learning models for factory and warehouse environments.
Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.
Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.
Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.
Ensure robust alerting and monitoring
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Position Description:
Ensure that ML models can be effectively developed, deployed, managed, and monitored in Production environments.
Productionize ML models – integrate trained ML models with Production systems
Build and manage ML pipelines – design, build, and maintain automated pipelines including data ingestion, data preprocessing, model training, validation, and deployment utilizing CI/CD practices.
Infrastructure management – set up and manage infrastructure for ML workloads uti
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