Forward deployed Engineer-6

Realign Llc; Toronto; Presencial; CAD 140 000 - 190 000; Tempo integral

Resumo da oferta

Realign Llc in Toronto, ON on-site seeks a Forward deployed Engineer with deep data engineering and AI application development experience. You will design, build, deploy, and operationalize enterprise-grade AI and data solutions for client engagements.

Responsibilities include designing scalable data pipelines, integrating Claude-powered workflows, managing MLOps, CI/CD, security, and governance while delivering measurable business outcomes with stakeholders.

Qualificações

• 10–14 years of experience in data engineering, AI app development, and production‑grade software.
• Hands‑on Python, PySpark, SQL, REST APIs, and FastAPI expertise.
• Experience building AI apps with Claude, prompt engineering, and RAG workflows.
• Understanding of ETL/ELT, batch/streaming pipelines, data quality and lineage.
• Hands‑on MLOps/LLMOps, MLflow, CI/CD, monitoring and production support.
• Experience with Git, Docker, Kubernetes, Airflow, and distributed data processing.
• Knowledge of cloud infra, security, and cost optimization.
• Strong security, IAM/RBAC, encryption, privacy, and compliance awareness.
• Ability to profile data, assess quality, coverage, and feasibility.
• Ability to prototype, validate, test, and operationalize AI/data solutions.
• Experience evaluating model errors and improving ML/LLM performance.
• Ownership of deployments, incidents, rollback, and reliability.
• Ability to translate business needs into scalable tech solutions.
• Strong client-facing consulting, workshop facilitation, and communication.
• Experience in BFSI environments and regulatory resilience requirements.
• Willingness to work onsite and travel for client engagements.
• Bachelor level or equivalent in CS/Engineering/IS.

Responsabilidades

• Design, develop, and deploy enterprise AI and data apps with Python, PySpark, Claude, SQL, and APIs.
• Collaborate with stakeholders to understand requirements and data landscapes.
• Define solution architecture, roadmaps, and technical approaches.
• Build scalable batch, real‑time, and streaming pipelines.
• Create Claude‑powered apps, copilots, and AI workflows with RAG and vector DBs.
• Profile data quality and readiness using Python, pandas, and PySpark.
• Prototype rapidly with client data and APIs; validate with users for production readiness.
• Implement CI/CD, automated testing, deployment automation, and infra practices.
• Operationalize MLOps/LLMOps, model lifecycle, monitoring, rollback, and support.
• Apply responsible AI, security, governance, traceability, and oversight.
• Troubleshoot data, API, identity, network, and deployment issues with clients.
• Create evaluation datasets and analyze model errors for improvements.
• Monitor production services, resolve incidents, and stabilize post‑go live.
• Measure user adoption and business impact via KPIs and dashboards.
• Develop accelerators, reference architectures, and playbooks.
• Lead client workshops, demos, estimates, and RFP support.
• Mentor teams and provide knowledge transfer.

Conhecimentos

Python

PySpark

SQL

REST APIs

FastAPI

Claude AI

RAG

MLOps

MLflow

CI/CD

Git

Docker

Kubernetes

Airflow

DataOps

Cloud Infra

IAM/RBAC

Security

BFSI

Prototyping

Data Profiling

Formação académica

Bachelor's Degree

Ferramentas

Git

Docker

Kubernetes

Airflow

Descrição da oferta de emprego

Location: Toronto, ON
Work Model: Onsite
Job Type: Full Time

Primary Skills

Python/PySpark, Claude & Agentic AI, SQL/APIs & MLOps

Job Description

We are seeking an experienced Forward deployed Engineer with strong expertise in Data Engineering, AI application development, LLM integration, and MLOps. The ideal candidate will work closely with client stakeholders, business users, and engineering teams to design, build, deploy, and operationalize enterprise‑grade AI and data solutions.

This role combines hands‑on software engineering, data discovery, solution architecture, rapid prototyping, and production ownership. The engineer will leverage Python, PySpark, SQL, Claude, REST APIs, RAG, and modern cloud data platforms to deliver scalable, secure, and measurable business outcomes.

Key Responsibilities

• Design, develop, and deploy enterprise‑grade AI and data applications using Python, PySpark, Claude, SQL, REST APIs, and cloud data platforms.
• Collaborate with business and technology stakeholders to understand requirements, workflows, data landscapes, and delivery objectives.
• Define solution architecture, implementation roadmaps, and technical approaches aligned with business goals.
• Design and implement scalable batch, real‑time, and streaming data pipelines.
• Build Claude‑powered applications, copilots, and AI workflows using prompt engineering, RAG, vector databases, semantic search, and agentic AI patterns.
• Perform data discovery and profiling using Python, pandas, PySpark, and SQL to assess data quality, coverage, volumes, and business‑process gaps.
• Develop rapid prototypes using real client data and APIs, validate solutions with business users, and iteratively prepare them for production.
• Implement and maintain CI/CD pipelines, automated testing, deployment automation, and infrastructure practices.
• Operationalize MLOps and LLMOps capabilities, including MLflow, model lifecycle management, monitoring, rollback, and production support.
• Apply responsible AI, security, privacy, governance, traceability, and human oversight controls.
• Troubleshoot data, API, identity, network, infrastructure, and deployment issues in collaboration with client teams.
• Create evaluation datasets, measure AI/LLM quality, conduct error analysis, and prioritize improvements.
• Monitor production services, resolve incidents, optimize performance and latency, and manage post‑go‑live stabilization.
• Measure user adoption and business impact using agreed KPIs, dashboards, and executive summaries.
• Develop reusable accelerators, reference architectures, engineering standards, runbooks, and implementation playbooks.
• Conduct client workshops, technical demonstrations, solution assessments, effort estimation, and RFP support.
• Mentor engineering teams and provide structured knowledge transfer and operational handover.

Required Skills & Experience

• 10–14 years of experience in Data Engineering, AI application development, cloud data platforms, APIs, and production‑grade software engineering.
• Strong hands‑on expertise in Python, PySpark, SQL, REST APIs, and FastAPI.
• Experience building AI‑powered applications using Claude, prompt engineering, RAG, vector databases, semantic search, and agentic AI.
• Strong understanding of ETL/ELT, batch and streaming pipelines, data quality, metadata management, data lineage, observability, and DataOps.
• Hands‑on experience with MLOps/LLMOps, MLflow, CI/CD, monitoring, deployment automation, and production support.
• Experience with Git, Docker, Kubernetes, Airflow, and distributed data processing frameworks.
• Knowledge of cloud infrastructure, data platforms, orchestration, security, networking, monitoring, and cost optimization.
• Strong understanding of enterprise security, IAM/RBAC, encryption, secrets management, privacy, auditability, and compliance.
• Experience profiling unfamiliar datasets and assessing data quality, coverage, distributions, edge cases, and feasibility.
• Ability to rapidly prototype, validate, test, secure, and operationalize AI and data solutions.
• Experience creating evaluation datasets, analyzing model errors, and improving ML/LLM performance.
• Demonstrated ownership of production deployments, incident resolution, rollback, stabilization, and service reliability.
• Ability to translate business requirements into scalable technical solutions and measurable outcomes.
• Strong client‑facing consulting, stakeholder management, workshop facilitation, and technical presentation skills.
• Experience working in Banking, Financial Services, or Insurance (BFSI) environments, with an understanding of regulatory and resilience requirements.
• Willingness to work onsite and travel when required for client engagements.
• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Management, or a related discipline, or equivalent relevant experience.

Preferred Skills

• Cloud platform, Kubernetes, AI Engineering, MLOps, Data Engineering, Enterprise Architecture, Security, or Program Delivery certifications.
• Experience embedding with client teams in a forward‑deployed delivery model.
• Experience measuring business outcomes such as time saved, error reduction, straight‑through processing, and productivity improvements.
• Strong technical leadership, mentoring, architecture review, and engineering best‑practice development skills.