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About This Role
- Will work on the intelligence layer for multiple programs — owns all model quality, RAG accuracy, prompt engineering, and AI safety across applications
- Socratic tutor persona, adaptive learning recommendation engine, multi\-modal AI (text and voice), RAG evaluation framework, and feedback loop into retrieval
- 6\-LLM call chain orchestration (NeMoGuardrails → intent classification → query rewriting → RAG → synthesis), , and compatibility check logic
- Production\-grade AI quality from launch — this is not a research or prototyping role; accuracy thresholds, latency requirements, and safety guardrails must pass InfoSec adversarial testing before Release 1
Required SkillsExperience
- Total IT 15 \+ Years
- 4–7 years of software engineering with at least 2 years focused on LLM application development in production — not research, not demos, not internal tools with 10 users
- Has shipped an LLM\-powered feature or product to production where real users depend on the accuracy and the engineer owns the quality metrics
- Has owned an AI safety or guardrails implementation for a customer\-facing product — not just added an off\-the\-shelf filter; designed and tested the safety layer
- Has built RAG evaluation pipelines and used them to make go/no\-go release decisions — accuracy gating is part of the workflow.
- Has profiled and optimized a multi\-step LLM call chain for latency
LLM Application Development
- LLM prompt engineering — system prompts, few\-shot examples, chain\-of\-thought, instruction following · Expert · Must\-have
- Multi\-step LLM chain orchestration — LangChain, LlamaIndex, or custom orchestration · Expert · Must\-have
- Multi\-turn conversation design — context window management, conversation summarization, session memory · Advanced · Must\-have
- Streaming LLM response handling — token\-by\-token streaming, partial response rendering · Advanced · Must\-have
- Model selection and benchmarking — matching model size to task; balancing latency, cost, and accuracy · Advanced · Must\-have
RAG Pipeline Design \& Quality
- RAG pipeline design — chunking strategy, embedding model selection, retrieval configuration · Expert · Must\-have
- Vector similarity search tuning — index parameters, similarity thresholds, retrieval depth · Advanced · Must\-have
- Reranking — cross\-encoder rerankers, relevance scoring · Advanced · Must\-have
- RAG evaluation frameworks — RAGAS, TruLens, or equivalent; automated eval pipelines · Advanced · Must\-have
- Hybrid search — combining dense vector retrieval with BM25 or keyword search · Proficient · Nice to have
AI Safety \& Guardrails
- Prompt injection detection and mitigation · Advanced · Must\-have
- Jailbreak testing and red\-teaming LLM systems · Advanced · Must\-have
- Content safety classifier integration · Advanced · Must\-have
- Hallucination detection and mitigation strategies · Advanced · Must\-have
- Topical control — enforcing scope boundaries on LLM responses · Advanced · Must\-have
Evaluation \& Production Quality
- Automated evaluation pipeline design — test set curation, metric selection, regression detection · Advanced · Must\-have
- A/B evaluation methodology for prompt and model changes · Proficient · Must\-have
- Latency profiling for LLM call chains — identifying bottlenecks across multi\-step pipelines · Proficient · Must\-have
- Feedback loop design — user signal collection, signal\-to\-retrieval\-weight integration · Proficient · Must\-have
- Production model monitoring — accuracy drift detection, quality degradation alerting · Proficient · Must\-have
Development
- Python — ML/AI application development, async programming · Expert · Must\-have
- API design for AI services — streaming endpoints, error handling, timeout management · Advanced · Must\-have
- Embedding model operations — model selection, batch embedding, index updates · Advanced · Must\-have
Nice to Have
- Adaptive learning systems or personalization engine experience
- Knowledge graph integration with RAG
- Multi\-agent orchestration patterns
- ServiceNow API integration
- Prior experience building AI products on NVIDIA infrastructure
Pay: $200\.00 \- $250\.00 per year
Experience:
- LLM Development : 4 years (Required)
- AI Architecture : 3 years (Required)
- LLM prompt engineering: 3 years (Required)
Work Location: In person
Salary Context
This $0K-$0K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Alchemy, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($225) sits 100% below the category median. Disclosed range: $200 to $250.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Alchemy AI Hiring
Alchemy has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Santa Clara, CA, US, US, Remote, US. Compensation range: $0K - $166K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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