Forward Deployed Engineer

Job summary

Embed with client teams to understand workflows and data, validate solutions using real data, and build and deploy enterprise AI and data applications. Own delivery from prototype through production launch and stabilization, including operational support, adoption measurement, and handover.

Job details

Forward Deployed Engineer Full Time Toronto, ON (Hybrid) 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 embed with business users, client SMEs, 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 • Embed with client SMEs, 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 client data using Python, pandas, PySpark, and SQL to assess coverage, quality, volumes, distributions, edge cases, and feasibility. • Build thin working solutions using real client 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 client SMEs, measure model and LLM quality, perform structured error analysis, and prioritize improvements. • Resolve data-access, API, identity, network, security, infrastructure, and deployment blockers with client 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. • BFSI 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.

What you’ll do

Embed with client teams to understand workflows and data, validate solutions using real data, and build and deploy enterprise AI and data applications. Own delivery from prototype through production launch and stabilization, including operational support, adoption measurement, 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. Candidates should have experience with Claude and modern AI patterns, strong client-facing and BFSI experience, and a relevant bachelor's degree.

Listed skills

• Kubernetes · Preferred
• SQL · Preferred
• REST APIs · Preferred
• CI/CD · Preferred
• Claude · Preferred
• prompt engineering · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• PySpark
• SQL
• REST APIs
• FastAPI
• Claude
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Vector Databases
• Semantic Search
• Agentic AI
• Data Engineering
• MLOps/LLMOps
• Kubernetes
• CI/CD
• Client-Facing Consulting

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Consulting

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