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Assistant Mlops Jobs (NOW HIRING)

Eliassen Group

Python MLOps Engineer

Almont, CO · On-site +1

$65 - $75/hr

Remote Our client seeks a Python-heavy MLOps engineer to own coding, debugging, and maintenance for ... These tools assist our hiring teams in different ways, including but not limited to, assistance in ...

AHEAD

Principal MLOps Platform Engineer

Chicago, IL · On-site

The MLOps Platform Engineer will own the deployment, IaC, observability, runtime management and ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

E-Solutions

MLOPS Engineer

Alpharetta, GA · On-site

... • Assist with documentation and governance of all ML and NLP pipeline artifacts • Find innovative solutions that increase automation and simplify work in AI workflows • Refactor and ...

Driscoll's

AI/MLOps Architect - R&D IT

Watsonville, CA · On-site

... assistant workflows. • Establish practical evaluation approaches for AI-enabled systems ... Required : • 5+ years of experience in machine learning engineering, platform engineering, MLOps ...

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Assistant Mlops information

What are Assistant MLOps?

Assistant MLOps are professionals who support the deployment, monitoring, and management of machine learning models in production environments. They assist senior MLOps engineers with tasks like automating workflows, managing data pipelines, maintaining infrastructure, and ensuring model performance. Their role bridges the gap between data science and IT operations, helping organizations scale and maintain their AI solutions efficiently. Assistant MLOps often have knowledge of cloud services, CI/CD tools, and basic programming, and they work closely with data scientists and engineers.

What is the difference between Assistant Mlops vs Data Engineer?

AspectAssistant MlopsData Engineer
Required CredentialsCertifications in cloud platforms, basic scripting, ML toolsComputer science degree, SQL, Python, data architecture
Work EnvironmentCollaborates with ML teams, supports deployment pipelinesBuilds data pipelines, manages databases, processes large datasets
Industry UsageAI/ML projects, cloud-based environmentsData infrastructure, analytics, big data solutions

Assistant Mlops and Data Engineer roles share overlapping skills in cloud platforms and scripting. However, Assistant Mlops focuses on supporting ML deployment and operations, while Data Engineers primarily build and maintain data infrastructure. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

What are some typical daily responsibilities for an Assistant MLOps professional?

As an Assistant MLOps professional, you can expect your daily tasks to involve supporting the deployment, monitoring, and maintenance of machine learning models in production environments. This often includes collaborating with data scientists to automate model training and testing workflows, managing cloud-based resources, and ensuring that data pipelines are running smoothly. You'll also help troubleshoot issues related to model performance or infrastructure and assist in implementing best practices for version control and continuous integration. Working closely with both engineering and data teams, you'll play a key role in ensuring that ML models remain reliable and scalable in real-world applications.

What are the key skills and qualifications needed to thrive as an Assistant MLOps, and why are they important?

To thrive as an Assistant MLOps, you need a solid understanding of machine learning fundamentals, programming (especially Python), and experience with cloud platforms; a degree in computer science or a related field is typically preferred. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and version control systems (e.g., Git) is important, and certifications in cloud services (AWS, Azure, GCP) can be advantageous. Strong problem-solving, communication, and collaboration skills help you bridge the gap between data science and operations teams. These combined skills ensure efficient deployment, monitoring, and maintenance of machine learning models in production environments.
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What are the most commonly searched types of Mlops jobs? The most popular types of Mlops jobs are:
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Assistant Mlops jobs near you
Infographic showing various Assistant Mlops job openings in the United States as of May 2026, with employment types broken down into 76% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 79% Physical, 5% Hybrid, and 16% Remote job distribution.

Principal Quant Developer (MLOps)

Fidelity Investments

Newark, NJ

$107K - $216K/yr

Full-time

Medical, Retirement, PTO

Posted 4 days ago

Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 264 frontline employees who took The Breakroom Quiz

14th of 138 rated financial services

Job description

Job Description:

Principal Quant Developer

The Role

The Quantitative Research & Investing Technology (QRIT) team within Fidelity's Asset Management Technology group is seeking a highly motivated and curious Principal Quantitative Developer. In this role you will contribute to a dynamic and fast-paced development team supporting researchers in prototyping and delivering new systematic investment strategies. You will provide high impact solutions on various projects including alpha research, portfolio construction, and risk management. Your technology knowledge covers a broad spectrum of technologies, including R, Python, and PL/SQL databases, positioning you as a full-stack software engineer who capitalizes on enterprise technology. You are committed to constructing high-quality, scalable, robust, resilient and efficient analytical and software solutions that propel investment processes forward.

You will possess:

  • A Bachelor's degree in Computer Science, Financial Engineering, Information Technology, Information Systems, Mathematics, Physics, Statistics, Engineering, or a closely related field and six (6) years of experience as a Senior Quant Developer or similar role.
  • Alternatively, a Master's degree (or equivalent foreign education) in the same fields, accompanied by four (4) years of experience as a Lead Quantitative Development or similar role.
  • This experience should include building high-quality, robust, and efficient systems and solutions for financial investment decisions, utilizing R, Python, PL/SQL databases, and quantitative techniques.

The Expertise and Skills You Bring

Core Engineering

  • Expert in Python with experience across the development stack (full stack)
  • Exposure to object-oriented programming (OOP) and design patterns
  • Experience in at least one unit testing framework and understanding of test-driven development (TDD) concepts and methodologies
  • A commitment to writing clean, maintainable, and efficient code, with best practices for long-term maintainability

Data & Infrastructure

  • Skilled in a range of database technologies: SQL (Oracle & Snowflake), NoSQL, Graph
  • Skilled in batch and API technologies: such as batch scheduling (using Autosys and Airflow) and creating REST APIs (using FAST API and Flask)
  • Proven ability to construct and manage robust data pipelines and event-driven workflows
  • Proven expertise in system design and cloud architecture on AWS, leveraging resources including Lambda, S3, EKS, and EC2


DevOps & CI/CD

  • Experience in containerization with Docker and orchestration with Kubernetes
  • Implement CI/CD pipelines (using Linux and Jenkins), code versioning using GitHub
  • Experience in Infrastructure as Code methodologies for consistent and scalable infrastructure management


MLOps & AI Infrastructure

  • Experience operationalizing machine learning models on AWS, including services such as SageMaker (training, deployment, model registry, monitoring) and Bedrock (foundation model access and fine-tuning)
  • Operationalizing AI/ML pipelines using modern MLOps principles, including production lifecycle management of AI models
  • Familiarity with experiment tracking and model versioning tools (e.g., MLflow)
  • Identifying and deploying applied ML solutions relevant to quantitative investing: time series forecasting, anomaly detection, NLP, and predictive analytics
  • Awareness of responsible AI governance practices
  • Demonstrated enthusiasm for contributing to all facets of our AI ecosystem, from application development to MLOps/LLMOps infrastructure, with a versatile, full-stack engineering mindset


Quantitative & Domain Knowledge

  • Demonstrated knowledge of mathematics, statistics, and quantitative finance
  • Deep understanding of quantitative techniques and methods, statistics and econometrics including probability, linear regression and time series data analysis
  • Analyze and design systems to implement quantitative models for systematic financial investments using R and Python, including time series forecasting models, multi-asset class portfolio construction strategies, risk management tools, alpha research, and simulation-based algorithms
  • Domain knowledge in either equities, fixed income or alternative asset classes
  • Progress towards CFA (or equivalent) a plus

Collaboration & Communication

  • Strong presentation and communication skills, with a knack for engaging with quant researchers and investment professionals
  • Strong problem-solving skills, with a proven ability to work effectively in cross-functional teams
  • A creative problem solver and a curiosity fueled by keeping up with advanced methodologies and industry trends, especially in the finance community
  • Lead the implementation of a research project through the entire software development lifecycle using a full-stack implementation
  • Assist Research teams in developing new models and products that will provide an advantage to the organization in the marketplace
  • Demonstrates eagerness and aptitude for rapidly adopting new frameworks, technologies, and best practices

The Team

The Quant Development team is part of Asset Management's Quantitative Research & Investment Technology group. We partner with Asset Management's Advance Strategies and Research team on cutting edge projects including systematic investment strategies, portfolio construction, risk management, alpha research, and GenAI. We build high quality, robust, and highly-scalable solutions that are used to improve Asset Management's efficiency and decision-making processes.

The base salary range for this position is $107,000-216,000 USD per year.

Placement in the range will vary based on job responsibilities and scope, geographic location, candidate’s relevant experience, and other factors.

Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.

Please be advised that Fidelity’s business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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