Senior Engineer, Machine Learning/AI Ops

Jobgether; United States; Remoto; USD 151.000 - 173.000; Tempo pieno

Vantaggi offerti da questo lavoro

Remote position in the US

Healthcare coverage

401(k) with employer matching

Paid leave

Parental leave

Life insurance

Transit benefits

Pet health insurance

Wellness program

Descrizione del lavoro

Jobgether is seeking a seasoned MLOps/platform engineering professional to design, deploy, and operate AWS-based ML and AI infrastructure. You will build event-driven integrations, CDC pipelines with Salesforce/Braze, and secure, scalable inference APIs. The role emphasizes IaC, observability, and collaboration with Data Science to productionize models.

You will lead incident response, scale reliable systems, and drive safe deployment practices across multiple environments in a remote US setting.

Competenze

• Six+ years in MLOps, platform engineering, DevOps, SRE, or related disciplines in production ML or data workloads.
• Bachelor's degree in a technical field.
• Deep hands-on AWS production workload experience across compute, messaging, security.
• Strong Python for production APIs and backend services.
• Experience building event-driven integrations with third-party APIs and CDC pipelines.
• Terraform and IaC across multiple environments.
• Practical experience deploying ML models with batch/real-time serving and monitoring.
• Experience with containerization, CI/CD, and safe production deployment practices.
• Hands-on PostgreSQL production experience, including migrations and indexing.
• Strong reliability, security, observability, and operational support knowledge.
• Ability to work independently, own outcomes, and make decisions.
• Excellent communication and cross-functional collaboration skills.
• Strong organizational and analytical skills to manage multiple projects.
• Preferred: experience with internal LLM/agent systems, ML observability, Snowflake workloads, and regulated industries.

Mansioni

• Design, build, deploy, and operate AWS infrastructure for ML models and AI tools.
• Develop event-driven integrations and CDC pipelines with Salesforce and Braze.
• Design reliable integration workflows with idempotency, replay, and schema evolution.
• Build and maintain containerized inference APIs and backend services with secure interfaces.
• Manage infrastructure as code with Terraform across AWS accounts and environments.
• Develop ML workflows for training, retraining, scoring, batch processing, and real-time inference.
• Collaborate with Data Science to move models from experimentation to production.
• Establish observability with logging, metrics, alerts, and model monitoring.
• Support secure operation of internal LLM and agentic systems with access controls and auditing.
• Collaborate with Data Engineering on Snowflake datasets and governance for ML workloads.
• Operate PostgreSQL-backed production services with migrations and indexing under concurrency.
• Lead incident response, on-call rotations, and runbooks to improve readiness.
• Establish platform engineering standards and safe deployment practices.
• Work cross-functionally to resolve integration challenges and maintain scalable AI infrastructure.

Conoscenze

MLOps

Platform engineering

DevOps

SRE

Python

AWS

Event-driven architectures

Terraform

CI/CD

PostgreSQL

Security

Observability

Formazione

Bachelor's degree in a relevant technical field

Strumenti

Snowflake

Salesforce

Braze

Amazon Bedrock

Descrizione del lavoro

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.