Forward Deployed Engineer (Data, ML & AI)

Remote

Pay: $500Pay period needs review — pay source
GreenShield+ Counselling Mental Health $500 in Health Care Spending Account annually Growth & Development
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Work setting: Remote — work setting source
Forward Deployed Engineer (Data, ML & AI) Location: Remote Timezone: NA/Eastern Type: Full-time Experience: 10+ Years Role Overview
Read the full posting
Employment: Full-time — employment source
Employment type in the employer’s posting: Full-Time - OG
From the employer’s posting

What you’ll work on

This position requires a Forward Deployed Engineer (FDE) specializing in Data, Machine Learning, and AI to embed directly within customer environments.

Deploy into customer environments and own delivery from data discovery to model integration
Refactor legacy data setups into modern lakehouses and streaming systems
Architect and deploy RAG pipelines and autonomous AI agents with evaluation frameworks
Build and maintain ML training and inference pipelines with CI/CD
Train customer teams on agentic patterns, MLOps and spec-driven development

From the employer’s posting

This position requires a Forward Deployed Engineer (FDE) specializing in Data, Machine Learning, and AI to embed directly within customer environments. You will serve as the primary technical authority transforming complex data challenges and operational bottlenecks into production-grade data pipelines, machine learning systems, and agentic AI solutions.

The role is heavily customer-facing: you will work alongside client business units and engineering teams to rapidly assess legacy data estates, build scalable modern data platforms, and deploy custom GenAI/LLM workflows that drive measurable business velocity. This is a high-ownership, hands-on role where you will leverage Spec-Driven Development (SDD) and AI-assisted workflows to build, refactor, and demonstrate immediate value directly where the customer operates.

Deploy directly into customer environments to understand their domain logic

Lead engineering efforts to refactor legacy data setups into modern lakehouses, event-driven streaming systems

Architect and deploy GenAI workflows, Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents

Build, deploy, and maintain robust ML training and inference pipelines

Elevate customer teams by establishing reusable agentic development patterns

What you’ll bring

All qualifications

Core experience

10+ Years of Experience: Proven track record as a Principal Data Engineer, Lead ML Engineer, or Enterprise Data Architect building and scaling distributed data and ML platforms.

Preferred experience

Prior experience in a Forward Deployed Engineer, Data Architect, or technical consulting/professional services role.
Experience migrating legacy, on-premise data warehouses or legacy Hadoop estates to modern cloud lakehouses.
Deep understanding of data governance, security compliance (HIPAA, SOC2, GDPR), and privacy-preserving machine learning.

Qualification wording

10+ Years of Experience: Proven track record as a Principal Data Engineer, Lead ML Engineer, or Enterprise Data Architect building and scaling distributed data and ML platforms.

Prior experience in a Forward Deployed Engineer, Data Architect, or technical consulting/professional services role.

Experience migrating legacy, on-premise data warehouses or legacy Hadoop estates to modern cloud lakehouses.

Deep understanding of data governance, security compliance (HIPAA, SOC2, GDPR), and privacy-preserving machine learning.

Apply at Carbon60

Posting record

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Status in our records: Live
First seen by us: Aug 8, 202687 days ago
Recorded sightings: 44
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 87

Up longer than most. Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

Carbon60 is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
Rust
Scala
SQL
TypeScript
AWS
Azure
Databricks
dbt
Docker
Hadoop
Kubernetes
MLflow
Redshift
Snowflake
Spark
Terraform
Airflow
Dagster
PySpark
Go

Where the posting names them

- Production MLOps & Orchestration: Build, deploy, and maintain robust ML training and inference pipelines using tools like MLflow, Kubeflow, Airflow, or Dagster. Ensure continuous integration/continuous deployment (CI/CD) for models and data workflows. - Polyglot Data Engineering: Design and audit production code across data-centric languages and frameworks (Python, SQL, Scala, Go, Rust, or TypeScript) based on speed, concurrency, and memory requirements. - Client Enablement & Knowledge Transfer: Elevate customer teams by establishing reusable agentic development patterns, modern MLOps practices, data reliability frameworks, and SDD methodologies so systems remain maintainable long after deployment.

- AI/ML & Vector Architecture: Hands-on experience fine-tuning, evaluating, and deploying LLMs, embedding models, and vector stores - Polyglot & Framework Proficiency: Advanced proficiency in Python and complex SQL, plus fluency in at least two other languages used in modern backend/data systems (e.g., Scala, Go, Rust, TypeScript). - Generative AI & SDD Experience: Demonstrated skill in using natural language and structured specs to guide AI tools (Claude Code, Cursor, Copilot) in generating data pipelines, schemas, and API adapters.

- Customer Embedding & Data Delivery: Deploy directly into customer environments to understand their domain logic, underlying data pipelines, and architectural constraints. Own end-to-end delivery—from data discovery and schema design to pipeline deployment and model integration—building trust as the customer's lead technical partner. - Data Platform & Architectural Modernization: Partner with client business leaders to evaluate legacy technology estates (e.g., monolithic SQL databases, or unmaintained ETL jobs). Lead engineering efforts to refactor legacy data setups into modern lakehouses, event-driven streaming systems, and scalable vector/graph databases. - Spec-Driven Development (SDD) for Data & AI: Apply a "spec-first" engineering workflow using Generative AI tools. Write structured specifications (data models, API schemas, transformations, and evaluation metrics) that instruct AI agents to generate production data models, PySpark jobs, data pipelines, and test suites.

- Data & ML Engineering Depth: - Data Infrastructure: Mastery of distributed computing (AWS Glue, Apache Spark, Databricks), modern data warehouses (Redshift, Snowflake, modeling tools (dbt), and data orchestration (Airflow, etc) - AI/ML & Vector Architecture: Hands-on experience fine-tuning, evaluating, and deploying LLMs, embedding models, and vector stores

- Generative AI & SDD Experience: Demonstrated skill in using natural language and structured specs to guide AI tools (Claude Code, Cursor, Copilot) in generating data pipelines, schemas, and API adapters. - Cloud & Infrastructure: Hands-on experience with cloud-native data services on AWS or Azure, containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform). - Willingness to Travel: Comfort with occasional travel to customer sites as needed.

- Prior experience in a Forward Deployed Engineer, Data Architect, or technical consulting/professional services role. - Experience migrating legacy, on-premise data warehouses or legacy Hadoop estates to modern cloud lakehouses. - Deep understanding of data governance, security compliance (HIPAA, SOC2, GDPR), and privacy-preserving machine learning.

- Agentic AI & LLMOps Implementation: Architect and deploy GenAI workflows, Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents using frameworks such as LangChain, LlamaIndex, or DSPy. Establish robust evaluation frameworks (Evals) for model accuracy, latency, and hallucination control. - Production MLOps & Orchestration: Build, deploy, and maintain robust ML training and inference pipelines using tools like MLflow, Kubeflow, Airflow, or Dagster. Ensure continuous integration/continuous deployment (CI/CD) for models and data workflows. - Polyglot Data Engineering: Design and audit production code across data-centric languages and frameworks (Python, SQL, Scala, Go, Rust, or TypeScript) based on speed, concurrency, and memory requirements.

- Data Platform & Architectural Modernization: Partner with client business leaders to evaluate legacy technology estates (e.g., monolithic SQL databases, or unmaintained ETL jobs). Lead engineering efforts to refactor legacy data setups into modern lakehouses, event-driven streaming systems, and scalable vector/graph databases. - Spec-Driven Development (SDD) for Data & AI: Apply a "spec-first" engineering workflow using Generative AI tools. Write structured specifications (data models, API schemas, transformations, and evaluation metrics) that instruct AI agents to generate production data models, PySpark jobs, data pipelines, and test suites. - Agentic AI & LLMOps Implementation: Architect and deploy GenAI workflows, Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents using frameworks such as LangChain, LlamaIndex, or DSPy. Establish robust evaluation frameworks (Evals) for model accuracy, latency, and hallucination control.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Forward Deployed Engineer (Data, ML & AI)

Location: Remote Timezone: NA/Eastern Type: Full-time Experience: 10+ Years

Role Overview

This position requires a Forward Deployed Engineer (FDE) specializing in Data, Machine Learning, and AI to embed directly within customer environments. You will serve as the primary technical authority transforming complex data challenges and operational bottlenecks into production-grade data pipelines, machine learning systems, and agentic AI solutions.

The role is heavily customer-facing: you will work alongside client business units and engineering teams to rapidly assess legacy data estates, build scalable modern data platforms, and deploy custom GenAI/LLM workflows that drive measurable business velocity. This is a high-ownership, hands-on role where you will leverage Spec-Driven Development (SDD) and AI-assisted workflows to build, refactor, and demonstrate immediate value directly where the customer operates.

Key Responsibilities

Customer Embedding & Data Delivery: Deploy directly into customer environments to understand their domain logic, underlying data pipelines, and architectural constraints. Own end-to-end delivery—from data discovery and schema design to pipeline deployment and model integration—building trust as the customer's lead technical partner.
Data Platform & Architectural Modernization: Partner with client business leaders to evaluate legacy technology estates (e.g., monolithic SQL databases, or unmaintained ETL jobs). Lead engineering efforts to refactor legacy data setups into modern lakehouses, event-driven streaming systems, and scalable vector/graph databases.
Spec-Driven Development (SDD) for Data & AI: Apply a "spec-first" engineering workflow using Generative AI tools. Write structured specifications (data models, API schemas, transformations, and evaluation metrics) that instruct AI agents to generate production data models, PySpark jobs, data pipelines, and test suites.
Agentic AI & LLMOps Implementation: Architect and deploy GenAI workflows, Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents using frameworks such as LangChain, LlamaIndex, or DSPy. Establish robust evaluation frameworks (Evals) for model accuracy, latency, and hallucination control.
Production MLOps & Orchestration: Build, deploy, and maintain robust ML training and inference pipelines using tools like MLflow, Kubeflow, Airflow, or Dagster. Ensure continuous integration/continuous deployment (CI/CD) for models and data workflows.
Polyglot Data Engineering: Design and audit production code across data-centric languages and frameworks (Python, SQL, Scala, Go, Rust, or TypeScript) based on speed, concurrency, and memory requirements.
Client Enablement & Knowledge Transfer: Elevate customer teams by establishing reusable agentic development patterns, modern MLOps practices, data reliability frameworks, and SDD methodologies so systems remain maintainable long after deployment.

Requirements

10+ Years of Experience: Proven track record as a Principal Data Engineer, Lead ML Engineer, or Enterprise Data Architect building and scaling distributed data and ML platforms.
Customer-Facing Aptitude: Strong executive presence and communication skills to interface directly with technical teams and business stakeholders under pressure.
Data & ML Engineering Depth:
- Data Infrastructure: Mastery of distributed computing (AWS Glue, Apache Spark, Databricks), modern data warehouses (Redshift, Snowflake, modeling tools (dbt), and data orchestration (Airflow, etc)
- AI/ML & Vector Architecture: Hands-on experience fine-tuning, evaluating, and deploying LLMs, embedding models, and vector stores
- Polyglot & Framework Proficiency: Advanced proficiency in Python and complex SQL, plus fluency in at least two other languages used in modern backend/data systems (e.g., Scala, Go, Rust, TypeScript).
Generative AI & SDD Experience: Demonstrated skill in using natural language and structured specs to guide AI tools (Claude Code, Cursor, Copilot) in generating data pipelines, schemas, and API adapters.
Cloud & Infrastructure: Hands-on experience with cloud-native data services on AWS or Azure, containerization (Docker, Kubernetes), and Infrastructure as Code (Terraform).
Willingness to Travel: Comfort with occasional travel to customer sites as needed.

Preferred Qualifications

Prior experience in a Forward Deployed Engineer, Data Architect, or technical consulting/professional services role.
Experience migrating legacy, on-premise data warehouses or legacy Hadoop estates to modern cloud lakehouses.
Deep understanding of data governance, security compliance (HIPAA, SOC2, GDPR), and privacy-preserving machine learning.

Compensation & Perks

Competitive compensation package (160K - 180K CAD / year)
Retirement Savings Matching Program (RRSP)
Access to the latest tech
Partnership with Perkopolis Discounts

Flexibility & Time Off

Remote first work environment
Flexible work hours & location
Paid parental leave options

Health & Wellness

Employer paid health & dental premiums
GreenShield+ Counselling Mental Health
$500 in Health Care Spending Account annually

Growth & Development

Peer recognition rewards

As an employer, OpsGuru, a Carbon60 Company, recognizes the importance of balancing our careers with other aspects of our lives, and our culture reflects this ethos - from flexible work hours to health and wellness incentives and having fun along the way. We look for people who thrive in an environment of accountability and at times ambiguity as we adapt and grow our business.

OpsGuru is an equal-opportunity employer. We welcome and encourage applications from people with all levels of ability. Accommodations are available on request for candidates taking part in all aspects of the selection process. We thank all applicants for their interest in this exciting opportunity.

Only candidates that meet the qualifications will be contacted for an interview.

Source details