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Astra-North Infoteck Inc. ~ Conquering today’s challenges, achieving tomorrow’s vision!

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

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Dec 4, 2026
Experience: 10+ years of experience in data engineering, AI application development, cloud data platforms, and production software engineering.
Posting language: English
Posted: Oct 13, 2026

Original job description

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

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.

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More about this job

Responsibilities

Partner with business users, subject matter experts, and engineering teams to understand workflows, assess real-world data, and deliver enterprise AI and data applications from prototype through production launch. Operate and improve deployed solutions by managing pipelines, model quality, monitoring, incidents, performance, adoption, and handover.

Requirements

Requires 10+ years of experience in data engineering, AI application development, cloud data platforms, and production software engineering, with hands-on expertise in Python, PySpark, SQL, APIs, cloud technologies, and MLOps/LLMOps. A relevant bachelor's degree, financial services experience, and strong client-facing skills are required; relevant cloud, Kubernetes, AI, data, architecture, MLOps, or security certifications are preferred.

Skills

Python
PySpark
SQL
REST APIs
FastAPI
Claude
Prompt Engineering
Retrieval-Augmented Generation
Vector Databases
Semantic Search
Agentic AI
Data Engineering
MLOps/LLMOps
Kubernetes
Client-Facing Consulting
Financial Services

Visa sponsorship

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Keywords

Python
PySpark
SQL
REST APIs
FastAPI
Claude
Prompt Engineering
Retrieval-Augmented Generation
Vector Databases
Semantic Search
Agentic AI
ETL/ELT
Batch Processing
Streaming Data Pipelines
Data Quality
DataOps
MLOps
LLMOps
MLflow
CI/CD
Docker
Kubernetes
Airflow
Distributed Processing
Cloud Data Platforms
IAM/RBAC
Financial Services
Model Monitoring
Data Lineage
Incident Management

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