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About This Role
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Avanta Labs, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Avanta Labs AI Hiring
Avanta Labs has 2 open AI roles right now. They're hiring across AI/ML Engineer.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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