Forward Deployed Engineer – AI, Data Engineering & MLOps / Toronto, ON (Onsite) - FTE

AceStack; Toronto; On-site; CAD 120,000 - 190,000; Full time

Job summary

AceStack seeks an experienced Forward Deployed Engineer to collaborate with client stakeholders and engineering teams in Toronto, onsite. You will design and deliver AI-powered data applications, build rapid prototypes, and scale solutions for production with robust MLOps practices.

You will profile client data, manage end-to-end pipelines, integrate Claude-powered workflows, and ensure governance, security, and observable production systems.

Qualifications

• Bachelor's degree or equivalent in CS/Engineering.
• 10–14 years of relevant experience across data engineering and AI.
• Experience delivering BFSI or regulated enterprise solutions and client-facing consulting.

Responsibilities

• Work directly with client stakeholders and engineering teams to discover problems, analyze data, and rapidly build AI-powered solutions.
• Deploy production-ready AI applications with measurable business outcomes.
• Lead workshops, architecture reviews, and code reviews, and provide effort estimates.
• Build reusable frameworks, accelerators, reference architectures, and runbooks.
• Mentor teams and ensure knowledge transfer and smooth handover.

Skills

Python

PySpark

SQL

Claude

APIs

FastAPI

LLM Engineering

MLOps

Docker

Kubernetes

DataOps

CI/CD

Observability

Data Profiling

RAG

Vector DBs

Cloud

Security

Governance

Production Engineering

Education

Bachelor's degree in CS/Engineering

Tools

Airflow

MLflow

Git

Job description

Location: Toronto, ON

Work Mode: Onsite

Experience: 10–14 Years

Job Description

We are seeking an experienced Forward Deployed Engineer with strong expertise in Python, PySpark, SQL, Claude, APIs, AI application development, and MLOps. The ideal candidate will work directly with client stakeholders and engineering teams to discover business problems, analyze real-world data, rapidly build AI-powered solutions, and deploy reliable, production-ready applications that deliver measurable business outcomes.

Must-Have Technical Skills

• Python, PySpark & SQL: Strong hands-on experience in data engineering, data profiling, distributed processing, and production-grade software development.
• AI & LLM Engineering: Experience with Claude, prompt engineering, Retrieval-Augmented Generation (RAG), vector databases, semantic search, and agentic AI workflows.
• APIs & Application Integration: Expertise in REST APIs, FastAPI, enterprise application integration, and connecting AI solutions to enterprise data sources.
• Data Engineering & DataOps: ETL/ELT, batch and streaming pipelines, data quality, metadata, lineage, orchestration, and observability.
• MLOps / LLMOps: Model lifecycle management, MLflow, CI/CD, automated deployment, model evaluation, monitoring, rollback, and production support.
• Cloud & Platform Engineering: Experience with cloud data platforms, Docker, Kubernetes, Airflow, Git, distributed computing, and infrastructure automation.
• Security & Governance: IAM/RBAC, encryption, secrets management, data privacy, auditability, compliance, and responsible AI controls.
• Production Engineering: Automated testing, performance optimization, error handling, reliability engineering, incident resolution, and operational monitoring.
• Data Discovery & Evaluation: Profiling unfamiliar datasets, assessing data quality and feasibility, building evaluation datasets, conducting error analysis, and improving AI solution quality.
• Forward-Deployed Delivery: Experience working directly with clients to conduct discovery, build rapid prototypes, integrate enterprise systems, deploy solutions, and measure business impact.
• Engage directly with client SMEs, business users, product owners, and engineering teams to understand workflows, business objectives, data sources, constraints, and success criteria.
• Design, develop, and deploy enterprise-grade AI and data applications using Python, PySpark, SQL, Claude, FastAPI, and REST APIs.
• Profile and analyze client data to assess data quality, coverage, volumes, distributions, edge cases, and business-process gaps.
• Build and operationalize Claude-powered applications, copilots, RAG pipelines, vector search solutions, and agentic AI workflows.
• Architect and implement scalable batch, streaming, and real-time data pipelines across enterprise data platforms.
• Rapidly develop prototypes using real client data and APIs, validate them with users, and iteratively harden solutions for production.
• Implement MLOps/LLMOps workflows covering evaluation, automated testing, CI/CD, deployment, monitoring, rollback, and lifecycle management.
• Establish responsible AI, security, privacy, traceability, governance, and human-oversight controls.
• Troubleshoot data-access, API, identity, networking, infrastructure, security, and deployment issues in collaboration with client teams.
• Own post-deployment stabilization, incident diagnosis, performance and latency optimization, cost monitoring, and production support.
• Measure adoption and business outcomes using KPIs such as productivity gains, time saved, error reduction, processing efficiency, and solution quality.
• Lead technical workshops, solution design, architecture reviews, code reviews, demonstrations, and effort estimation.
• Develop reusable frameworks, accelerators, reference architectures, runbooks, and implementation standards.
• Mentor engineering teams and ensure effective knowledge transfer, documentation, operational handover, and ongoing support readiness.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Management, or a related discipline; equivalent relevant experience may be considered.
• 10–14 years of relevant experience across data engineering, AI application development, cloud data platforms, APIs, and production software engineering.
• Experience delivering solutions in Banking, Financial Services, Insurance (BFSI), or other regulated enterprise environments.
• Strong client-facing consulting, problem-solving, communication, and technical presentation skills.
• Demonstrated ability to take solutions from discovery and rapid prototyping through production deployment, adoption, and measurable business outcomes.
• Willingness to work onsite with client teams and travel when required.

Preferred: Certifications in cloud platforms, AI engineering, MLOps, data engineering, Kubernetes, enterprise architecture, or security.