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

Toronto, ON
On-site
Posted Oct 16, 2026
1 position

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Employment type: Full-time
Experience level: Lead · 10+ years
Minimum education: Bachelor’s degree
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level
Application method: Direct apply is available

Job summary

Work directly with client stakeholders and engineering teams to discover business needs, assess data, and design, prototype, and deploy production-ready AI and data applications. Operationalize AI and data solutions with secure, reliable pipelines and MLOps/LLMOps practices, then support deployments and measure business outcomes.

Job details

Job Title: Forward Deployed Engineer – AI, Data Engineering & MLOps 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. Roles & Responsibilities 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.

What you’ll do

Requirements

Requires 10–14 years of relevant experience across data engineering, AI application development, cloud data platforms, APIs, and production software engineering, along with a bachelor's degree or equivalent relevant experience. Candidates should have strong client-facing consulting and communication skills, experience in regulated enterprise environments, and the ability to deliver solutions from discovery through production and measurable outcomes.