Senior Data AI Engineer – GenAI & Agentic AI

ON
Hybrid
Posted Oct 16, 2026
1 position

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Employment type: Full-time
Experience level: Lead · 14+ years
Minimum education: Bachelor’s degree
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level
Application method: Apply on LinkedIn (LinkedIn sign-in required)

Job summary

Design, build, deploy, and support enterprise AI and data solutions, including data pipelines, GenAI applications, and production MLOps/LLMOps capabilities. Work directly with client stakeholders to translate business needs into scalable solutions, lead technical delivery, and support adoption, governance, and operational reliability.

Job details

Position: Forward Deployed Engineer (Python/PySpark + Claude AI + Data & MLOps) Job type: Full-time/Permanent hiring Location: Hybrid/Onsite- Toronto, ON || New Jersey, New York City, Charlotte, Columbus (OH), Pittsburgh, Boston, Malvern (PA), Washington DC USA Locations (14 openings) Canada Location (11 openings) We are seeking an experienced Forward Deployed Engineer (FDE) to design, build, and deploy enterprise-scale AI and data-driven solutions for leading Banking, Financial Services, and Insurance (BFSI) clients. The ideal candidate will possess strong expertise in Data Engineering, Generative AI, Large Language Models (LLMs), Cloud Technologies, APIs, and MLOps/LLMOps. In this role, you will work directly with business stakeholders, client SMEs, architects, and engineering teams to transform complex business requirements into scalable, production-ready AI solutions. You will be responsible for full lifecycle delivery, from discovery and prototyping through deployment, operationalization, and post-production support. Key Responsibilities Design, develop, and deploy enterprise-grade AI and data applications using Python, PySpark, SQL, Claude AI, APIs, and modern cloud platforms. Build AI-powered solutions utilizing Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases, Semantic Search, and Agentic AI frameworks. Architect scalable data pipelines, data platforms, and AI solutions with focus on performance, security, reliability, and cost optimization. Develop and support batch, streaming, and real-time data processing solutions. Implement MLOps and LLMOps frameworks including CI/CD, model lifecycle management, monitoring, deployment automation, and production support. Collaborate closely with business stakeholders to understand requirements and translate them into technical designs and implementation roadmaps. Conduct data discovery, profiling, quality assessment, and feasibility analysis on large and diverse datasets. Build rapid prototypes and proof-of-concepts using real business data and APIs. Implement security, governance, privacy, and compliance controls across AI and data solutions. Troubleshoot and resolve production issues, performance bottlenecks, and platform incidents. Support solutioning activities, technical workshops, client presentations, and RFP responses. Mentor engineering teams and contribute to best practices, reusable frameworks, and technical standards. Work directly with client teams in a forward-deployed model to accelerate business outcomes and solution adoption. Required Technical Skills Programming & Data Engineering Python PySpark SQL ETL/ELT Development Data Pipelines Batch and Streaming Processing DataOps AI / Generative AI Claude AI Large Language Models (LLMs) Prompt Engineering Agentic AI Retrieval-Augmented Generation (RAG) Vector Databases Semantic Search AI Copilots & AI Workflow Automation API & Application Development REST APIs FastAPI Enterprise Application Integration Microservices MLOps / LLMOps MLflow CI/CD Pipelines Model Lifecycle Management Monitoring & Observability Production Deployment Rollback & Recovery Strategies Platform & DevOps Docker Kubernetes Git Airflow Cloud Platforms Infrastructure Automation Security & Governance IAM/RBAC Data Security Data Privacy Encryption Secrets Management Compliance & Auditability Domain Experience Strong experience within one or more of the following: Banking Financial Services Insurance (BFSI) Experience working in regulated, secure, and highly governed enterprise environments is highly preferred. Required Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, Data Management, or related discipline. 10-14+ years of experience in Data Engineering, AI Applications, Cloud Platforms, and Software Engineering. Proven experience building and deploying AI/GenAI solutions into production. Strong client-facing and consulting experience. Ability to lead architecture discussions and provide technical leadership. Experience working directly with business stakeholders and cross-functional teams. Preferred Certifications Cloud Platform Certifications (AWS, Azure, GCP) Kubernetes Certifications AI Engineering Certifications Data Engineering Certifications Enterprise Architecture Certifications Security Certifications

What you’ll do

Requirements

Requires a bachelor's degree in a related discipline and 10–14+ years of experience in data engineering, AI applications, cloud platforms, and software engineering, including production deployment of AI/GenAI solutions. Candidates should have strong client-facing consulting and technical leadership experience, with expertise in Python, PySpark, SQL, cloud technologies, APIs, and MLOps/LLMOps.