... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
... product quantization is a plus. * Experience with embeddings, ANN/KNN, vector stores, database ... Foundational understanding of Natural Language Processing and Deep Learning. * Excellent problem ...
Deep Learning Quantization information
What are the key skills and qualifications needed to thrive as a Deep Learning Quantization Engineer, and why are they important?
To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.
What is the difference between Deep Learning Quantization vs Machine Learning Engineer?
| Aspect | Deep Learning Quantization | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Advanced degrees in AI, Computer Science, or related fields; knowledge of neural networks | Bachelor's or Master's in CS, Data Science, or related fields; programming skills |
| Work Environment | Research labs, AI development teams, hardware optimization settings | Software development teams, data-driven projects, product-focused environments |
| Industry Usage | AI hardware optimization, model deployment, edge computing | Model development, data analysis, software solutions across industries |
Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.
What is deep learning quantization?
Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.
What are some common challenges faced when implementing deep learning quantization in production environments?
One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.
What are popular job titles related to Deep Learning Quantization jobs in Connecticut? For Deep Learning Quantization jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Connecticut look for? The top searched job categories for Deep Learning Quantization jobs in Connecticut are:
Full-time
Posted 17 days ago
The Hartford rating
8.8
Based on 110 frontline employees who took The Breakroom Quiz
53rd of 281 rated insurance
Job description
Sr Data Engineer - GE07BE
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.
The Hartford seeks energetic and passionate AI Platform Engineers to build AI Operations (AIOps, MLOps, FMOps, LLMOps) services for our AI Platform team. Our team supports data science and business teams across the enterprise, driving streamlined processes and smarter decision-making in functions like Actuarial, Product, Underwriting, and Sales. As an AI Engineer within the AI Operations organization, you will play a significant role in delivering modern and sustainable data science products generating meaningful outcomes for the enterprise.
We are looking for talent who embraces our core values:
Solution-Oriented: We build solutions, not just models, focusing on end-to-end business problems and systems design.
Trust and Transparency:We collaborate closely with partners, mindful of their capacity to absorb change.
Reliability: Our products come with full monitoring solutions to ensure they deliver as expected.
Influence through Humility: We listen carefully to our customers and become partners in problem-solving.
Practical and Evolutionary: We deliver minimally viable products first and expand their sophistication over time based on feedback.
This role can have a Hybrid or Remote work arrangement. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday). Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise.
Responsibilities:
Develop value-added features tailored to company specific work, above and beyond the core capabilities of the cloud platform and relevant vendor tools.
Research, experiment with, and implement suitable GenAI algorithms, tools, and technologies.
Explore new services and capabilities in AWS, Google Cloud Platform, and Azure to support GenAI and ML services.
Enhance platform functionality with strong engineering expertise in AI, ML, Agentic Frameworks, and modern data technologies.
Develop and promote best practices in AI, ML, and data engineering across teams.
Architect and design end-to-end solutions at a component level.
Collaborate with partners in Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams.
Manage engineering tasks, driving execution, and optimizing workflows with minimal guidance.
Provide technical mentorship and career growth opportunities for team members.
Review work of systems-level engineers to calibrate deliverables against project and business expectations.
Qualifications:
Bachelor's degree in Computer Science, Computer Engineering, or a technical field.
8+ years building and shipping software and/or platform solutions for enterprises.
Programming experience with Python is preferred.
3+ years of experience with Terraform.
Experience building libraries, frameworks or platforms used across multiple teams is a plus.
Proven experience with Google Cloud Platform (GCP).
Experience with GCP BigQuery, Cloud Functions, AI Platform, API Gateway, GKE/Docker is a must.
Proven experience in working with other cloud providers such as AWS cloud is a plus.
Experience with CI/CD pipelines, Automated Testing, Automated Deployments, Agile methodologies, Unit Testing, and Integration Testing tools.
Experience with building scalable serverless applications (real-time/batch) using cloud technologies. GCP is a plus.
Knowledge of distributed NoSQL database systems and data engineering, ETL technology.
Conversational UX/UI design (chatbots) and Human-Agent-Interaction (HAI) is a plus.
Experience with IR, vector embedding, and Hybrid/Semantic search technologies.
Knowledge about customization techniques across various stages of the RAG pipeline, including model fine-tuning, retrieval re-ranking, HNSW, and product quantization is a plus.
Experience with embeddings, ANN/KNN, vector stores, database optimization, & performance tuning is a plus.
Experience with LLM orchestration frameworks like Langchain, LlamaIndex, LangSmith, LangGraph, Google Agent Development Kit, is a plus.
Experience with Generative AI Guardrails, responsible AI, adversarial attack mitigation, and red teaming is a plus.
Foundational understanding of Natural Language Processing and Deep Learning.
Excellent problem-solving skills and the ability to work in a collaborative team environment.
Excellent communication skills.
Candidate must be authorized to work in the US without company sponsorship.The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$117,200 - $175,800Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
About Us|Our Culture|What It's Like to Work Here|Perks & Benefits
What The Hartford employees say
Pay
Benefits
Hours and flexibility
Workplace
Get the full story on Breakroom
About Hartford
Sourced by ZipRecruiter
Hartford Financial Services Group, widely recognized as The Hartford, is a renowned company based in Hartford, CT, US. Established in 1810, it has evolved into an industry leader in the insurance and financial services sector, proudly serving more than one million businesses in the US. The Hartford is committed to offering a gamut of insurance products that include homeowners, automobile, and business insurance as well as employee benefits and mutual funds. The company’s core values revolve around customer-focused innovations, diversity and inclusion, and ethical dealings that have earned them a customer-centric reputation. This shapes their mission which revolves around aiding their clients to overcome unforeseen obstacles and enhancing their wealth over time. Among the company's noted accomplishments is being consistently listed among the World's Most Ethical Companies, a testament to their unwavering commitment towards responsible business practices.
Industry
Finance and insurance
Company size
10,000+ Employees
Headquarters location
Hartford, CT, US
Year founded
1810