Forward Deployed AI Engineer (Remote)

About Nexla

Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and  APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces.

Leading companies including DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp trust Nexla for mission-critical data. Named in the 2022, 2023, and 2024 Gartner Magic Quadrant™ for Data Integration Tools and top-rated by customers on Gartner Peer Insights, Nexla is a remote-first company headquartered in San Mateo, California.

At Nexla, our culture is built around our core values: Have Empathy, Be Curious, Be Intellectually Honest, Achieve Excellence, and Remember to Relax. We put our customers at the heart of everything we do, foster a data-driven mindset, take ownership of our work, and believe in the power of teamwork to achieve ambitious goals.

Role Description

We're pioneering the integration of AI capabilities into our unified data operations platform, working closely with customers to enhance their data workflows with intelligent automation and predictions. Our hands-on approach involves deeply understanding customer needs, implementing AI solutions within their data operations, and ensuring successful adoption of our intelligent features. Currently, onboarding a new customer involves making customer data GenAI-ready and using our novel AI engine to solve real business use-cases. However, since AI is still very much a newer technology, we require specialized expertise and dedicated attention in especially understanding business use-cases to generate ROI.

As we scale our AI capabilities across our customer base, we're looking for a Forward Deployed AI Engineer who is passionate about implementing AI solutions, running successful PoC’s, and making our customers successful.

Day-to-Day

This role combines elements of several key positions:

• AI Implementation Engineer: Use LLM’s to solve customer-specific problems.
• Data Scientist: Analyze customer data patterns, build AI models 
• ML Engineer: Design and implement ML models for tasks like Anomaly Detection
• Product Owner: Take full ownership of AI feature success, demonstrate entrepreneurial spirit in solving customer challenges
• Technical Consultant: Help customers understand and maximize value from our AI capabilities

Who You Are

Here's what we believe would make someone successful in this role:

• Strong Understanding of ML/AI - Strong knowledge of ML/AI algorithms, model deployment, and AI implementation patterns
• Data engineering foundation - Advanced Python, SQL
• Production AI experience - Track record of building products on top of LLM’s in production environments
• Customer-centric mindset - Excellent communicator who can explain complex AI concepts to technical and non-technical stakeholders
• Fast learner - Ability to quickly understand new use cases and adapt AI solutions to different business contexts
• Ownership Mentality - Intrinsically motivated to make customers successful with strong technical chops
• High velocity - You love building and shipping software that delights customers at a high-pace.

Requirements

• Experience with ML/AI models
• Proficiency in data preprocessing
• Understanding of ML ops and model serving architectures with a perspective on LLMOps
• Knowledge of AI performance optimization and troubleshooting
• Familiarity with distributed computing and large-scale data processing

Ideal Background:

• 2+ years of experience in AI/ML implementation roles
• Track record of owning end-to-end AI projects
• Background in production ML systems and intelligent data pipelines

Bonus Points

• Knowledge of various AI model deployment patterns
• Familiarity with enterprise AI governance requirements
• Have worked with PyTorch and Ray

This is an opportunity to shape the future of intelligent data operations, working at the intersection of AI and data engineering while directly impacting customer success and product evolution.