Senior ML Engineer / MLOps Platform Engineer

Senior ML Engineer / MLOps Platform Engineer

100% Remote | Full-time | Senior | 5+ years | Fluent English

About BorionAI

BorionAI is a boutique AI implementation firm built around one principle: AI should not stop at prototypes, notebooks, or slide decks.

We deploy production-ready AI systems that work in real business environments, on timelines measured in weeks, and we tie our compensation to delivery, not hours billed.

We work with enterprises tired of long consulting engagements, stalled pilots, and AI projects that never reach production. Our model is different: small senior teams, fast execution, clear ownership, fixed-scope delivery, and practical systems that connect data, models, APIs, workflows, and business users.

We are not a strategy firm, a staff-augmentation shop, or a slide-deck factory. We are an implementation firm.

About the role

We are looking for a Senior ML Engineer / MLOps Platform Engineer to help us deploy real AI systems into production.

This role sits at the intersection of Software Engineering, Machine Learning Engineering, MLOps, cloud infrastructure, and Agentic AI deployment. You will work closely with Data Scientists and AI Engineers to turn models, pipelines, and prototypes into reliable production systems.

The ideal candidate is not just someone who knows ML tools. We need someone who can build the production layer around AI: APIs, orchestration, model endpoints, cloud data flows, CI/CD, logging, monitoring, authentication, scalability, and service design.

Why work with us

This is a 100% remote role for people who like building real systems.

You will work directly on production AI, ML, and Agentic AI solutions instead of getting stuck in layers of process. The work is hands-on, technical, and outcome-driven: pipelines, APIs, model endpoints, service layers, cloud deployments, orchestration, CI/CD, monitoring, and client-facing AI products.

Our focus is deployment, deployment, deployment: production-ready systems delivered in less than 90 days whenever scope allows.

BorionAI is a senior, execution-focused team: high standards, low bureaucracy, and a clear delivery mindset. We are looking for senior builders who want ownership, room to design, deploy, simplify, and make technical decisions that directly affect delivery.

What you will do

• Build and deploy production-grade ML and Agentic AI pipelines.
• Design data flows across S3, ADLS, GCS, and other cloud storage systems.
• Expose ML models via batch and real-time inference endpoints.
• Build the service layer between ML, APIs, web apps, and client tools.
• Build REST APIs for model access, predictions, and integrations.
• Test and document APIs with Postman or similar tools.
• Implement authentication, logging, monitoring, and observability.

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• Build CI/CD pipelines for ML and software services.
• Containerize and deploy services with Docker; Kubernetes when needed.
• Deploy to AWS, Azure, or GCP environments.
• Improve latency, throughput, cost, reliability, and maintainability.
• Productionize models and close the gap between notebooks and systems.
• Support Agentic AI workflows involving LLMs, tools, APIs, RAG, and function calling.
• Work under pressure with clear timelines and delivery commitments.
• Communicate technical decisions clearly, internally and with clients.

What we are looking for

• 5+ years of experience in Software Engineering, ML Engineering, MLOps, or a closely related role.
• Strong Python engineering skills.
• Fluent English, written and spoken.
• Experience building and deploying REST APIs using FastAPI, Flask, Django, Spring Boot, or similar.
• Experience deploying ML models or data products to production.
• Experience with orchestration tools such as Airflow, Prefect, Dagster, Kubeflow, or similar.
• Experience with cloud platforms: AWS, Azure, or GCP.
• Experience with storage and data systems: S3, ADLS, GCS, data lakes, warehouses, or lakehouses.
• Solid database fundamentals
• Experience with MLflow or similar tools for tracking, registry, versioning, and lifecycle management.
• Experience with Docker.
• Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar.
• Strong understanding of system design, API design, and production service patterns.
• Experience with large-scale data tools such as Spark, Databricks, Dask, Ray, or similar.
• Practical understanding of batch vs real-time inference.
• Ability to reason about latency, throughput, scalability, cost, and observability.
• Consulting mindset and strong ownership: able to move from ambiguity to a clean production workflow.

Bonus points

• Kubernetes.
• Databricks.
• LLM / Agentic AI deployment.
• RAG and GraphRAG.
• Vector databases.
• FastAPI.
• Terraform or infrastructure as code.
• Web app deployment.
• Model monitoring and drift detection.
• Client integrations.
• Internal ML platforms.

First 90 days

• Deploy at least one ML or Agentic AI workflow as a production-ready endpoint.
• Set up a basic orchestration pipeline for training, scoring, or inference.
• Implement a clean API layer exposing model predictions or AI workflow outputs.
• Add logging, monitoring, and basic observability to at least one production workflow.
• Improve reliability, performance, or maintainability of an existing ML, data, or service component.
• Create reusable patterns that make future AI deployments faster and cleaner.

Hiring bar

We are looking for engineers who have built real systems end-to-end.

The strongest candidates can explain trade-offs clearly, simplify complex systems, and connect data, models, APIs, cloud infrastructure, and client-facing applications into one reliable production workflow.

Tool knowledge matters, but ownership, engineering judgment, fluent communication, consulting maturity, and production experience matter more.

Apply

Send your CV and a short note on why this role fits to:

careers@borion.ai

Subject line:

MLOps Engineer · BAI-ENG-001