Forward Deployed Generative AI Engineering

About Avanta Labs

Avanta Labs helps ambitious small and mid-market companies build practical AI that changes how work gets done—in weeks, not quarters. We deliver systems from model selection through production deployment that customers can understand, operate, and own. Our work spans agentic solutions, AI enablement, AI-native software, business transformation, small language models, strategy, governance, adoption, measurement, and executive advisory.

About the Forward Deployed Engineering Team

Our Forward Deployed Engineering team works with customers to turn business problems into secure, production-ready AI. Engineers collaborate with users and operating teams to build, test, deploy, document, and improve solutions beyond the demo. You will learn Avanta Labs’ delivery patterns while building application components under experienced engineers.

Role Summary

You will build well-scoped generative AI applications and integrations using approved architectures, managed services, and established engineering practices. You will contribute dependable code, thoughtful testing, clear documentation, and a positive customer experience.

Role Mission

Help customers move from a defined need to a working, supported AI capability—one reliable component at a time.

What You Will Do

• Build chatbots, copilots, assistants, and automations for repeatable business tasks.
• Integrate foundation-model APIs and managed GenAI services into customer applications.
• Implement prompt templates, structured outputs, tool calls, and bounded conversational flows.
• Build basic Retrieval-Augmented Generation (RAG) pipelines using approved ingestion, chunking, embedding, retrieval, and citation patterns.
• Connect apps to APIs, databases, document repositories, SaaS tools, and approved enterprise data.
• Develop the UI, backend, API, or integration needed for an assigned feature.
• Deploy services using established cloud, container, authentication, secrets, storage, and monitoring patterns.
• Create evaluation examples, run predefined test suites, and document quality findings.
• Troubleshoot common prompt, retrieval, tool-use, integration, latency, and model-response problems.
• Contribute architecture notes, configurations, test evidence, runbooks, and customer documentation.
• Join code reviews, sprint planning, demos, retrospectives, and knowledge-sharing.
• Raise security, privacy, quality, scalability, or delivery risks early; seek help when beyond your scope.

Customer-Facing Responsibilities

• Join customer discovery sessions and ask clear questions about users, workflows, data, and expected outcomes.
• Translate well-defined requirements into manageable technical tasks with support from the project lead.
• Demonstrate working features in language business users and engineers can follow.
• Track assigned actions, communicate progress, and surface blockers before they affect the customer timeline.
• Respond to feedback professionally and adjust implementation details without losing quality.
• Build trust through preparation, honesty, responsiveness, and reliable execution.
• Avoid overstating certainty, model capability, or delivery outcomes.

Technical Responsibilities

• Write maintainable, tested code in Python, TypeScript, Java, C#, or a similar language.
• Use REST APIs, JSON, authentication, web services, and integration patterns.
• Apply version control, debugging, automated tests, code review, and CI/CD practices.
• Manage prompt templates and structured model outputs, including JSON validation and error handling.
• Apply foundational RAG concepts, including chunking, embeddings, semantic search, retrieval, context construction, and citations.
• Implement simple agents or workflow steps with a small number of approved tools and clearly bounded permissions.
• Add logging, metrics, traces, dashboards, and failure handling using established patterns.
• Follow Avanta Labs and customer standards for identity, access, secrets, sensitive data, responsible AI, and secure development.
• Compare model responses using defined quality criteria and explain observed trade-offs.

What This Role Is Not Expected to Own

• Independently architecting a large multitenant AI platform or complex distributed system.
• Training a foundation model from scratch or selecting specialized hardware independently.
• Leading a major enterprise transformation, contractual scope negotiation, or high-risk architecture decision without support.
• Owning an entire customer account, critical production escalation, or organization-wide governance program.

Required Qualifications

• Approximately 1–3 years of relevant experience in software engineering, data, automation, machine learning, or equivalent practical work.
• Hands-on exposure to generative AI through production work, internships, applied research, open-source projects, or substantial personal projects.
• Sound programming fundamentals in Python, TypeScript, Java, C#, or a comparable language.
• Working knowledge of APIs, databases, testing, debugging, version control, and cloud application delivery.
• Basic understanding of foundation models, prompts, RAG, vector search, agent tools, model limitations, and responsible-AI risks.
• Ability to write clear technical documentation and communicate professionally with teammates and customer contributors.
• Demonstrated curiosity, reliability, attention to detail, and willingness to learn unfamiliar tools.
• Equivalent experience and demonstrated capability may substitute for a specific degree or tenure requirement.

Preferred Qualifications

• Internship or project experience delivering a generative AI, search, document-processing, or productivity application.
• Experience with a frontend framework, enterprise identity system, vector database, or document-ingestion pipeline.
• Cloud or developer certification relevant to the role.
• Open-source contributions, research projects, hackathons, or technical writing that demonstrate practical learning.
• Experience supporting internal business users or customer-facing technical teams.

Representative Technologies

Candidates do not need experience with every product below. We value strong fundamentals and the ability to learn equivalent technologies quickly.

Languages and application development

Python; TypeScript/JavaScript; Java; C#; SQL; FastAPI; Flask; Node.js; React or comparable frameworks.

Model platforms

Amazon Bedrock; Azure OpenAI or Azure AI Foundry; Google Vertex AI; OpenAI APIs; Anthropic APIs; Hugging Face; managed or open-weight equivalents.

RAG and application frameworks

Native provider SDKs; LangChain; LangGraph; LlamaIndex; Semantic Kernel; Haystack; approved workflow frameworks.

Retrieval and data

PostgreSQL/pgvector; OpenSearch/Elasticsearch; Pinecone; Weaviate; Milvus; Qdrant; Redis; object storage; managed databases.

Infrastructure and delivery

Docker; serverless services; API gateways; GitHub Actions or equivalent CI/CD; basic Terraform or cloud-native templates.

Evaluation and observability

Custom test harnesses; cloud-native logging and monitoring; OpenTelemetry; Prometheus/Grafana; RAG evaluation tools.

Security

Identity and access management; role-based access control; encryption; secrets management; audit logging; model guardrails.

Core Competencies and Soft Skills

• Curiosity and coachability.
• Reliable ownership of assigned work.
• Clear written and verbal communication.
• Empathy for users and customer teammates.
• Attention to detail and testing discipline.
• Collaborative problem-solving.
• Comfort learning in a fast-changing technical environment.
• Ability to work through ambiguity with guidance.
• Professional judgment about when to escalate.

Scope, Autonomy, and Decision-Making

You will own clearly bounded features, integrations, tests, and documentation. A mid-level or principal engineer will provide architectural direction and review higher-risk decisions. You may recommend implementation changes, but you will not independently approve security-sensitive designs, complex distributed architectures, unbounded agent actions, or major platform commitments.

How Success Will Be Measured

• Delivers components meeting functional, quality, security, and documentation standards.
• Writes tested, readable, maintainable code and responds constructively to review feedback.
• Resolves common defects and integration problems without creating unnecessary complexity.
• Runs evaluations accurately and records results in a form the team can use.
• Learns and applies Avanta Labs’ approved architectural and delivery patterns.
• Communicates progress and risk early enough for the team to act.
• Earns trust from colleagues and customer teams through reliable execution.

Expected Outcomes During the First 6–12 Months

• Ship components in a customer solution and support testing or production release.
• Build or extend one RAG application, workflow automation, or bounded agent using an approved reference pattern.
• Contribute reusable code, templates, tests, or documentation to Avanta Labs’ delivery library.
• Demonstrate sound handling of sensitive data, identity, secrets, logging, and model limitations.
• Present working features confidently and explain their operation to customers.
• Show readiness to own a small workstream with decreasing day-to-day supervision.

Travel or Customer-Site Expectations

Travel may be needed for discovery, implementation, workshops, or go-live support. Expectations and work-location model will be confirmed before hire.

Reporting Relationship

Reports to a Forward Deployed Engineering lead, technical lead, or manager designated by Avanta Labs’ leadership. Final assignment to be confirmed.

Compensation and Benefits

Compensation and benefits are to be confirmed. Avanta Labs will share the employment structure, compensation, healthcare, paid time off, and benefits before an offer.

Equal Opportunity and Accessibility Statement

Avanta Labs is an equal opportunity employer. We consider qualified applicants without regard to any status protected by applicable law and provide reasonable accommodations throughout recruiting and employment. Candidates may request an accommodation when applying or interviewing.