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About This Role
About Avanta Labs
Avanta Labs helps small and mid-market companies deploy practical AI in weeks, not quarters. We build customer-owned systems spanning agentic AI, AI-native software, small language models, business transformation, strategy, governance, adoption, measurement, and advisory.
About the Forward Deployed Engineering Team
We combine senior technical judgment with hands-on delivery. Principals lead programs, develop engineers, and turn customer needs into reusable Avanta Labs platforms and IP.
Role Summary
Translate business objectives into AI programs, architect GenAI/ML systems, lead production delivery, and serve as the strategic technical escalation point.
Role Mission
Build secure, scalable, economically defensible AI capabilities from executive objectives.
What You Will Do
- Architect AI systems across data, retrieval, models, agents, applications, evaluation, governance, and operations.
- Lead strategy through production, adoption, and managed operations.
- Select managed/open-weight models, traditional ML, fine-tuned/distilled models, or custom small language models based on evidence and economics.
- Evaluate prompt engineering, RAG, PEFT, pretraining, preference optimization, distillation, quantization, pruning, and custom training.
- Design domain-specific SLMs for privacy, edge, latency, and cost needs; guide data curation, labeling, lineage, and reproducibility.
- Architect training, inference, serving, and routing; optimize quality, latency, throughput, hardware use, reliability, and cost.
- Build safe single/multi-agent designs with tool contracts, state, memory, planning, permissions, human approvals, and containment of unsafe or unreliable behavior.
- Define offline/online, human, automated, adversarial, safety, and business evaluations, release gates, drift controls, rollback, and incident response.
- Lead security, identity, privacy, responsible AI, model risk, and supply-chain architecture.
- Evaluate build-versus-buy and vendors; establish reference architectures, platforms, and standards.
- Lead incidents, mentor engineers, and shape the product roadmap.
Customer-Facing Responsibilities
- Lead discovery with executives, architects, security leaders, business owners, and delivery teams.
- Turn ambiguous goals into sequenced programs with measurable outcomes, architecture, governance, and operating models.
- Prioritize by value, feasibility, data readiness, risk, adoption, reuse, and cost.
- Present investment and architecture trade-offs; challenge weak, unsafe, or unjustified proposals.
- Negotiate scope, sequencing, constraints, customer responsibilities, and risk ownership.
- Guide adoption and production issues while maintaining trusted, hands-on customer relationships.
- Support strategic accounts, due diligence, proposals, and executive presentations.
Technical Responsibilities
- Apply transformers, SFT, LoRA/QLoRA, adapters, preference optimization, distillation, quantization, pruning, and compression.
- Design distributed training/inference, checkpointing, lineage, experiment tracking, registries, and reproducible releases.
- Optimize serving with batching, caching, routing, cascades, speculative decoding, autoscaling, scheduling, and capacity planning.
- Design multitenant, event-driven, asynchronous, hybrid-cloud, edge, and disconnected systems.
- Establish CI/CD, MLOps, LLMOps, infrastructure/policy-as-code, resilience, recovery, and continuity.
- Implement IAM, authorization, data boundaries, sandboxing, isolation, audit, and policy controls for autonomous actions.
- Evaluate model, RAG, agent, safety, infrastructure, and business performance, including LLM-as-judge limitations; use A/B tests, drift monitoring, tracing, and root-cause analysis.
- Model full lifecycle cost; set acceptance criteria and ownership before go-live.
What This Role Is Not Expected to Own
- Sales quotas or primarily sales engineering.
- Treating LLMs, agents, fine-tuning, or training as the answer to every problem.
- Detached advisory work without production involvement.
- Unilateral commercial, legal, or capital commitments beyond delegated authority.
Required Qualifications
- Mastery of Associate and Forward Deployed GenAI Engineer capabilities.
- Approximately 8–12+ years in engineering, ML, platforms, architecture, or technical leadership, including complex production AI/data systems.
- Hands-on code review, prototyping, full-stack diagnosis, and systems architecture.
- Leadership of major customer programs involving data, applications, security, operations, and change.
- Expertise in model selection, RAG, agents, evaluations, adaptation, performance, security, governance, and operations.
- Sound build-versus-buy, platform, infrastructure, and operating-model judgment under uncertainty.
- Credibility with boards, executives, and engineers; mentorship, crisis leadership, cross-team influence, and reusable IP creation.
- Equivalent experience may replace degree or tenure requirements.
Preferred Qualifications
- Foundation-model or domain-specific SLM work; research, patents, open source, or advanced model engineering.
- GPU/accelerator operations, distributed training, or hardware-aware optimization.
- Regulated, safety-critical, privacy-sensitive, edge, or disconnected environments.
- Enterprise AI governance, internal AI platforms, or forward-deployed practice design.
- Confidential computing, federated learning, or privacy-preserving ML.
- Vendor, partnership, investment, or acquisition evaluation; board/industry presentations.
Representative Technologies
Equivalent technologies welcome; not all products are required.
Languages: Python; TypeScript/JavaScript; Java; C#; Go; SQL.
Cloud/models: AWS Bedrock/SageMaker; Azure AI Foundry/OpenAI; Vertex AI; OpenAI; Anthropic; Hugging Face; open-weight models.
Model engineering: PyTorch; TensorFlow; JAX; Transformers; PEFT; DeepSpeed; Accelerate; Ray.
Inference: vLLM; TGI; NVIDIA Triton; ONNX; TensorRT; MLX; gateways and serving.
Agents: LangGraph; Semantic Kernel; AutoGen; CrewAI; LlamaIndex; SDKs; tool protocols; sandboxing.
Data/retrieval: PostgreSQL/pgvector; OpenSearch/Elasticsearch; Pinecone; Weaviate; Milvus; Qdrant; Redis; Neo4j; streaming/lakehouse/document systems.
Evaluation: MLflow; Weights & Biases; LangSmith; Arize Phoenix; OpenTelemetry; Prometheus; Grafana.
Infrastructure/security: Docker; Kubernetes; Terraform; CI/CD; Argo CD; serverless; service meshes; IAM; RBAC/ABAC; encryption; DLP; audit; guardrails.
Core Competencies and Soft Skills
- Architectural judgment, executive communication, commercial awareness.
- Customer empathy and influence under uncertainty.
- Mentorship, crisis leadership, negotiation, and stakeholder management.
- Clear writing, risk judgment, and hands-on leadership.
- Converting delivery knowledge into reusable standards and IP.
Scope, Autonomy, and Decision-Making
Lead architecture for assigned strategic engagements and approve standards, reference patterns, release gates, and platform choices within CTO-delegated authority. Escalate material company investment, legal, customer-risk, or product-strategy decisions to Avanta Labs' CTO and executive team.
How Success Will Be Measured
- Deliver production AI systems linked to measurable customer outcomes.
- Make sound model, architecture, platform, and security decisions.
- Reduce delivery time, operational risk, and complexity using reusable standards.
- Establish evaluation, governance, observability, and release practices.
- Develop technical leaders and resolve severe drift, quality, latency, cost, security, or reliability issues.
- Influence customer strategy, create reusable IP, and build executive trust.
Expected Outcomes During the First 6–12 Months
- Lead a strategic program from executive framing through production.
- Improve a reference architecture, evaluation standard, governance control, or delivery platform.
- Standardize build-versus-buy and model-selection decisions.
- Develop an engineer to independently lead a complex workstream.
- Turn repeated customer needs into a reusable accelerator or product-roadmap proposal.
- Demonstrate gains in quality, risk, customer value, cost, or deployment speed.
- Represent Avanta Labs in executive and architecture reviews.
Travel or Customer-Site Expectations
Travel may be required for discovery, workshops, executive reviews, delivery, and escalations; final expectations will be confirmed before hire.
Reporting Relationship
Reports to an Avanta Labs technical leader designated by executive leadership. May lead engineering pods and mentor engineers across engagements.
Compensation and Benefits
Compensation and benefits are to be confirmed. Avanta Labs will share 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 legally protected status and provide reasonable accommodations during recruiting and employment. Candidates may request accommodations 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. Senior-level AI roles across all categories have a median of $229,300.
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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