Forward Deployed Engineer (Data, ML & AI)
At a glance
Mid-level Software Engineering role at carbon60. Toronto · full-time · CA$160,000–CA$180,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Work inside customer environments to map domain rules, existing data flows and architecture limits before shaping a delivery plan
- Take data work from discovery and schema design through pipeline rollout and connecting models
- Modernize older data systems into lakehouses, streaming platforms and scalable vector or graph databases
- Specify data models, APIs, transformations and evaluation measures so AI tools can generate pipelines, jobs and tests
- Deploy RAG workflows and AI agents, then assess accuracy, latency and hallucinations in production
What you bring
- At least ten years building distributed data and ML platforms in senior data engineering, ML engineering or architecture roles
- You bring advanced Python and SQL skills, plus working fluency in two other backend or data languages
- You have hands-on experience with LLMs, embedding models and vector stores, including tuning, evaluation and deployment
- You can guide AI coding tools with structured specifications and have used AWS or Azure alongside Docker, Kubernetes and Terraform
Who this fits
This suits a senior, hands-on engineer who can work directly with customer business and engineering teams, communicate with senior stakeholders, and stay effective under pressure. The role is remote, aligned to North American Eastern hours, and may involve occasional customer-site travel. It calls for substantial individual technical ownership and customer knowledge transfer.
From the employer
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.
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