Senior Engineer, Machine Learning/AI Ops

Jobgether; United States; Remote; USD 151,000 - 173,000; Full time

Benefits offered by this job

Health coverage

Retirement plan

Paid leave

Parental leave

Life insurance

Transit benefits

Pet health insurance

Wellness program

Mentorship

Job summary

Jobgether is seeking a Senior Engineer, Machine Learning/AI Ops based in the United States. The role combines software engineering with hands-on AWS, ML ops, and production platform engineering to transform experiments into scalable services.

You will design real-time inference APIs, manage data pipelines, and strengthen observability while ensuring secure, auditable AI infrastructure in a security-conscious, data-driven environment.

Qualifications

• Bachelor's degree in a technical field.
• Hands-on experience operating production workloads on AWS.
• Strong Python programming skills for production APIs and backend services.
• Experience building event-driven integrations with third-party APIs and CDC pipelines.

Responsibilities

• Design, build, deploy, and operate AWS infrastructure for ML models and AI tools.
• Develop and maintain event-driven integrations and CDC pipelines between internal systems and third-party platforms.
• Build and maintain containerized inference APIs and production-grade services with secure interfaces.

Skills

Python

Communication

Independence

On-call

Education

Bachelor's degree

Tools

Terraform

PostgreSQL

Docker

AWS

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Engineer, Machine Learning/AI Ops based in the United States.

The Senior Engineer, Machine Learning/AI Ops will design, build, and operate the cloud infrastructure powering machine learning models, internal AI tools, and event-driven integrations. This role combines strong software engineering expertise with hands-on experience in AWS, machine learning operations, infrastructure automation, and production platform engineering. Working closely with Data Science, Data Engineering, Analytics, Product, and other engineering teams, the successful candidate will help transform experimental models into reliable, scalable production services. Key responsibilities include developing real-time inference APIs, managing data integration pipelines, strengthening system observability, and maintaining secure, auditable AI infrastructure. The role also supports the responsible operation of large language models and agentic systems while improving deployment practices, reliability, and incident response. This is an opportunity for an autonomous technical professional to influence platform engineering standards and help advance AI capabilities in a security-conscious, data-driven environment.

Accountabilities

• Design, build, deploy, and operate AWS infrastructure and services supporting machine learning models, internal AI tools, and event-driven integrations.
• Develop and maintain event-driven integrations and change data capture (CDC) pipelines between internal systems and third-party platforms, including Salesforce and Braze.
• Design reliable and recoverable integration workflows that account for idempotency, event replay, schema evolution, out-of-order events, and failure handling.
• Build, deploy, and maintain containerized inference APIs and backend services with secure, versioned interfaces and production-grade performance.
• Manage infrastructure as code using Terraform across multiple AWS accounts and environments, leveraging reusable modules and CI-gated workflows.
• Develop and maintain machine learning workflows for training, retraining, scoring, batch processing, and real-time inference.
• Partner with Data Science teams to establish repeatable processes for moving models from experimentation into production.
• Implement observability across machine-learning platforms, AI tools, and integration services, including logging, metrics, alerting, and model monitoring.
• Support the secure operation of internal large language model (LLM) and agentic systems, including solutions built with Amazon Bedrock, through appropriate access controls, auditing, and data protection measures.
• Collaborate with Data Engineering teams on Snowflake datasets, data access, and governance requirements supporting machine learning and operational workloads.
• Operate and maintain PostgreSQL-backed production services, ensuring reliable schema migrations, effective indexing, and data correctness under concurrent writes.
• Lead incident response activities, participate in on-call rotations, and develop runbooks and reusable engineering patterns that improve operational readiness.
• Establish and promote platform engineering standards, safe deployment practices, and continuous improvement across production systems.
• Work cross-functionally to identify technical risks, resolve integration challenges, and maintain reliable, secure, and scalable AI infrastructure.

Requirements

• At least six years of experience in MLOps, platform engineering, DevOps, site reliability engineering (SRE), backend engineering, or a related discipline, including substantial experience supporting production machine learning, data, or event-driven workloads.
• A bachelor's degree in a relevant technical field.
• Deep, hands-on experience designing and operating production workloads on AWS, including compute, messaging, infrastructure, and security services.
• Strong professional Python programming skills, with experience building and operating production APIs, backend services, and integrations.
• Demonstrated experience building event-driven integrations with third-party APIs, including change data capture pipelines.
• Strong Terraform and infrastructure-as-code expertise across multiple production environments.
• Practical experience deploying and operating machine learning models, including batch and real-time serving, model versioning, and monitoring.
• Experience with containerization, CI/CD pipelines, and safe, reliable production deployment practices.
• Hands-on experience operating PostgreSQL in production, including schema migrations, indexing strategies, and maintaining data integrity under concurrent writes.
• Strong understanding of production system reliability, security, observability, troubleshooting, and operational support.
• The ability to work independently, take ownership of outcomes, and make sound technical decisions without relying on prescriptive instructions.
• Excellent communication and collaboration skills, with the ability to influence engineering standards and work effectively across technical disciplines.
• Strong organizational and analytical skills, including the ability to manage multiple projects, reprioritize quickly, and deliver high-quality results in a fast-paced environment.
• Preferred: Experience operating or securing internal LLM-based applications and agentic systems.
• Preferred: Familiarity with machine learning observability, experiment tracking, or model drift detection tools.
• Preferred: Hands-on experience with Snowflake supporting machine learning or production data workloads.
• Preferred: Experience in financial services, fintech, or another regulated industry.
• Availability for a regular full-time position, with the ability to participate in on-call support as required.

Benefits

• Annual salary: $150,500-$173,000 USD, with individual compensation determined by location, relevant skills, experience, education, and training.
• Work arrangement: Remote position within the United States.
• Healthcare coverage: Medical, dental, and vision benefits.
• Retirement savings: 401(k) plan with employer matching.
• Paid leave: Paid holidays, vacation, sick days, and volunteer time off.
• Parental leave: 12 weeks of paid parental leave.
• Insurance: Life insurance at no cost to employees, along with additional voluntary benefits.
• Transportation: Pre-tax transit benefits.
• Pet care: Discounted ASPCA pet health insurance.
• Wellness: Employee wellness incentive program.
• Career development: Mentorship and leadership programs, with opportunities for professional growth.
• Work environment: A collaborative, team-oriented culture focused on employee development and continuous improvement.

Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

We appreciate your interest and wish you the best!