Senior Forward Deployed AI Engineer (Python, Data & GenAI)

Astra-North Infoteck Inc. ~ Conquering today’s challenges, achieving tomorrow’s vision!; Toronto; On-site; CAD 130,000 - 190,000; Full time

Job summary

Astra-North Infoteck Inc. in Toronto searches for a senior Forward Deployed Engineer who blends production-grade software engineering with data engineering, AI application development, and client-facing consulting.

You will work with business users, SMEs, product owners, and engineers to map workflows, validate solutions with real data, remove blockers, and own delivery through production launch, stabilization, adoption, and handover.

Qualifications

• Bachelor's degree in a relevant discipline.
• Cloud, Kubernetes, AI engineering, MLOps, data engineering, architecture or security certifications preferred.
• Strong client-facing skills and experience in discovery, rapid prototyping, and workshops.

Responsibilities

• Map workflows with experts, product owners, and business users to understand data and decision points.
• Agree on release milestones and a roadmap from prototype to production.
• Profile data with Python, PySpark, and SQL to assess quality, volumes, and edge cases.
• Build solutions using real data and APIs, then validate with users and productionize.
• Design and deploy AI/data applications using Python, PySpark, SQL, REST APIs, FastAPI, and cloud platforms.
• Develop Claude-powered apps, copilots, and AI workflows with prompt engineering, RAG, and vector databases.
• Create data pipelines (batch, real-time, streaming) across enterprise systems.
• Implement MLOps/LLMOps, including lifecycle management, CI/CD, monitoring, and deployment automation.
• Develop evaluation datasets, measure model quality, perform error analysis, and prioritize improvements.
• Resolve data access, API, identity, security, and deployment blockers with technical teams.
• Own post-go-live monitoring, incident diagnosis, rollback, and cost optimization.
• Measure adoption and business impact, then hand over runbooks and knowledge transfer.

Skills

Python

PySpark

SQL

REST APIs

FastAPI

Git

Docker

Kubernetes

Airflow

MLflow

CI/CD

Distributed processing

MLOps

LLMOps

Claude

DataOps

Cloud platforms

IAM/RBAC

Security/compliance

Education

Bachelor's degree in a relevant discipline

Tools

Git

Docker

Kubernetes

Airflow

MLflow

Job description

The Opportunity

We are looking for a senior Forward Deployed Engineer who combines production-grade software engineering, data engineering, AI application development, and client-facing consulting. You will work closely with business users, subject matter experts, product owners, and engineering teams to understand complex workflows, rapidly validate solutions using real data, remove technical blockers, and own delivery through production launch, stabilization, adoption, and handover.

What You Will Do

• Work with subject matter experts, product owners, business users, and engineers to map workflows, users, data, constraints, and decision points.
• Agree on the first useful release, measurable success criteria, and a practical roadmap from prototype to production.
• Profile unfamiliar data using Python, pandas, PySpark, and SQL to assess coverage, quality, volumes, distributions, edge cases, and feasibility.
• Build working solutions using real data and APIs, validate them with users, and iteratively harden them for production.
• Design, develop, and deploy enterprise AI and data applications using Python, PySpark, SQL, REST APIs, FastAPI, and cloud data platforms.
• Build Claude-powered applications, copilots, and AI workflows using prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.
• Design batch, real-time, and streaming data pipelines and integrations across enterprise systems.
• Implement MLOps/LLMOps capabilities, including lifecycle management, CI/CD, MLflow, monitoring, deployment automation, rollback, and production support.
• Create evaluation datasets with subject matter experts, measure model and LLM quality, perform structured error analysis, and prioritize improvements.
• Resolve data-access, API, identity, network, security, infrastructure, and deployment blockers with technical teams.
• Own post-go-live monitoring, incident diagnosis, rollback, stabilization, latency, performance, reliability, and cost optimization.
• Measure adoption and business impact using agreed KPIs, then complete structured handover with runbooks, support ownership, and knowledge transfer.

What You Bring

• 10+ years of experience in data engineering, AI application development, cloud data platforms, and production software engineering.
• Strong hands-on expertise in Python, PySpark, SQL, REST APIs, FastAPI, Git, Docker, Kubernetes, Airflow, MLflow, CI/CD, and distributed processing.
• Experience with Claude, prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.
• Strong knowledge of ETL/ELT, batch and streaming pipelines, data quality, metadata, lineage, observability, and DataOps.
• Experience operationalizing MLOps/LLMOps, including model lifecycle management, automated deployment, monitoring, rollback, and production support.
• Knowledge of enterprise cloud services across compute, storage, integration, orchestration, networking, monitoring, security, and cost optimization.
• Understanding of IAM/RBAC, encryption, secrets management, privacy controls, auditability, compliance, and secure data access.
• Ability to translate business requirements into scalable designs, define non-functional requirements, estimate effort, and drive implementation.
• Financial services experience and strong client-facing skills across discovery, rapid prototyping, solution shaping, workshops, and technical presentations.
• Bachelor's degree in a relevant discipline. Cloud, Kubernetes, AI engineering, MLOps, data engineering, architecture, or security certifications are preferred.