Only w2--Mid level-Forward deployed Engineer--Local to Dallas

Invovia; Frisco (TX); Vor Ort; USD 180.000 - 240.000; Vollzeit

Zusammenfassung

Invovia is seeking an experienced Forward Deployed Engineer to bridge AI, software engineering, and customer success. You will embed with enterprise teams, prototype quickly, and deliver production-grade AI solutions aligned to business outcomes.

You will own end-to-end delivery from discovery to deployment, working across AI applications, data pipelines, and cloud platforms, while mentoring peers and shaping engineering practices in a fast-growing organization.

Qualifikationen

• 6+ years of professional software engineering experience building and deploying production applications.
• Proven experience owning complex technical projects from concept through production with minimal supervision.
• Demonstrated success working directly with enterprise customers in solution delivery or consulting-oriented engineering roles.

Aufgaben

• Partner closely with internal teams and enterprise customers to understand business workflows, operational challenges, and strategic objectives.
• Translate customer requirements into scalable AI-powered software solutions that deliver measurable business value.
• Serve as the primary technical advisor throughout solution delivery, building trusted relationships with customer stakeholders.
• Design, develop, and deploy production-grade agentic AI applications, including voice agents, chat agents, AI copilots, and workflow automation solutions.
• Build Retrieval-Augmented Generation (RAG) pipelines, prompt orchestration frameworks, and multi-agent systems tailored to customer-specific use cases.
• Develop robust prompt engineering strategies and evaluation frameworks to ensure accuracy, reliability, and performance of AI systems.
• Rapidly prototype proof-of-concepts, incorporate customer feedback, and transition successful solutions into production on an iterative delivery cycle.
• Integrate AI applications with enterprise data sources, APIs, third-party platforms, and business applications.
• Build and maintain scalable data ingestion pipelines to prepare structured and unstructured data for AI workloads.
• Navigate complex enterprise environments involving heterogeneous systems and legacy integrations.
• Own the complete deployment lifecycle, including implementation, monitoring, optimization, and ongoing support.
• Continuously improve solution reliability, scalability, latency, and user adoption after deployment.
• Troubleshoot production issues and implement long-term improvements to enhance customer success.
• Partner with Product and Engineering teams to communicate recurring customer challenges and influence product roadmap decisions.
• Establish engineering best practices, reusable frameworks, deployment playbooks, and operational standards for the Forward Deployed Engineering function.
• Mentor junior engineers and contribute to building a high-performing engineering culture.

Kenntnisse

Python

LLM-powered applications

Prompt Engineering

RAG pipelines

Agentic AI

Multi-agent systems

AI evaluation

REST APIs

Cloud platforms

CI/CD

Tools

LangChain

LlamaIndex

Snowflake

Databricks

Docker

AWS

Azure

Google Cloud Platform

Pinecone

Milvus

pgvector

Jobbeschreibung

About the Role

We are looking for an experienced Forward Deployed Engineer (FDE) to work at the intersection of AI, software engineering, and customer success. In this role, you will partner closely with enterprise customers to understand their business challenges and design, build, and deploy production-grade AI solutions that deliver measurable business outcomes.

As a Forward Deployed Engineer, you will embed with customer teams, rapidly prototype solutions, and take them to production using modern AI technologies, cloud platforms, and enterprise integrations. You will own the complete delivery lifecycle—from problem discovery through deployment, adoption, and continuous optimization.

Key Responsibilities

• Partner closely with internal teams and enterprise customers to understand business workflows, operational challenges, and strategic objectives.
• Translate customer requirements into scalable AI-powered software solutions that deliver measurable business value.
• Serve as the primary technical advisor throughout solution delivery, building trusted relationships with customer stakeholders.

AI Solution Development

• Design, develop, and deploy production-grade agentic AI applications, including voice agents, chat agents, AI copilots, and workflow automation solutions.
• Build Retrieval-Augmented Generation (RAG) pipelines, prompt orchestration frameworks, and multi-agent systems tailored to customer-specific use cases.
• Develop robust prompt engineering strategies and evaluation frameworks to ensure accuracy, reliability, and performance of AI systems.
• Rapidly prototype proof-of-concepts, incorporate customer feedback, and transition successful solutions into production on an iterative delivery cycle.
• Integrate AI applications with enterprise data sources, APIs, third-party platforms, and business applications.
• Build and maintain scalable data ingestion pipelines to prepare structured and unstructured data for AI workloads.
• Navigate complex enterprise environments involving heterogeneous systems and legacy integrations.

Production Ownership

• Own the complete deployment lifecycle, including implementation, monitoring, optimization, and ongoing support.
• Continuously improve solution reliability, scalability, latency, and user adoption after deployment.
• Troubleshoot production issues and implement long-term improvements to enhance customer success.
• Partner with Product and Engineering teams to communicate recurring customer challenges and influence product roadmap decisions.
• Establish engineering best practices, reusable frameworks, deployment playbooks, and operational standards for the Forward Deployed Engineering function.
• Mentor junior engineers and contribute to building a high-performing engineering culture.

Required Qualifications

Experience

• 6+ years of professional software engineering experience building and deploying production applications.
• Proven experience owning complex technical projects from concept through production with minimal supervision.
• Demonstrated success working directly with enterprise customers in solution delivery or consulting-oriented engineering roles.

Software Engineering

• Expert-level proficiency in Python.
• Deep understanding of software architecture, distributed systems, REST APIs, and full-stack application development.
• Hands-on experience deploying LLM-powered applications into production environments.
• Strong expertise in:
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Agentic AI architectures
• Multi-agent orchestration
• AI evaluation and optimization
• Experience with frameworks such as LangChain, LlamaIndex, or equivalent orchestration platforms.
• Strong SQL skills with experience designing and optimizing complex queries.
• Experience building ETL/ELT pipelines and working with enterprise data platforms.
• Hands-on experience with modern data warehouses such as Snowflake, Databricks, or equivalent platforms.
• Ability to transform and prepare enterprise datasets for AI applications.
• Experience deploying applications on AWS, Azure, or Google Cloud Platform.
• Hands-on experience with Docker containers, CI/CD pipelines, infrastructure automation, and production deployments.
• Familiarity with cloud-native architectures and scalable application deployment practices.
• Strong customer-facing experience delivering enterprise software solutions.
• Ability to navigate ambiguous environments, gather requirements, conduct technical workshops, and communicate effectively with technical teams and executive stakeholders.
• Excellent written and verbal communication skills.

Leadership

• Experience leading technical initiatives and influencing engineering decisions.
• Ability to mentor engineers and establish engineering best practices.
• Strong ownership mindset with a bias toward execution, continuous learning, and rapid iteration.

Preferred Qualifications

• Experience with MLOps practices, including model versioning, experimentation frameworks, monitoring, and observability.
• Experience with vector databases such as Pinecone, Milvus, pgvector, or equivalent technologies.
• Experience deploying AI solutions across enterprise domains such as customer support, retail, finance, supply chain, or manufacturing.
• Strong understanding of enterprise data ecosystems and governance.
• Experience building new engineering functions or working as an early member of a high-growth engineering team.

What Makes You Successful

• You thrive in ambiguous, fast-paced environments.
• You enjoy solving difficult customer problems with practical engineering solutions.
• You balance rapid experimentation with production-quality engineering.
• You are equally comfortable discussing system architecture with engineers and business outcomes with executives.
• You take ownership of customer outcomes, not just software delivery.