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About This Role
3Pillar is an AI transformation partner on a mission to help enterprises build the AI\-native products and intelligent agents that will define the next era of business. With teams across North America, Europe, Latin America, and Asia, we work with the most ambitious companies in financial services, healthcare, media, and technology — helping them move faster, modernize boldly, and compete on their own terms. Our HelixAI platform and Helix Pods delivery model put our engineers at the center of real agentic transformation — doing work that is open, portable, and built to last. We are building the future of enterprise AI.
AI Data Architect
We are looking for an AI Data Architect to design, build, govern, and evolve the single source of truth that powers every AI initiative in our organization.
This platform will serve as the foundational nervous system for conversational AI assistants, dashboard intelligence, autonomous AI agents, RAG\-powered applications, predictive ML models, and any AI product we build today or in the future. The resource will architect the system, drive implementation, own the data contracts that agents and AI applications depend on, enforce security and access governance for both human and agent consumers, and continuously monitor and improve the accuracy and reliability of AI outputs that flow from this platform.
### Requirements:
Architect and own the enterprise AI data platform — the unified, governed layer that ingests, transforms, stores, and serves all data consumed by AI systems across the organisation.
Design multi\-domain data models (lakehouse, data mesh, event\-driven) that are structured from day one to serve AI workloads: clean lineage, versioned schemas, well\-documented contracts, and low\-latency serving APIs.
Strong exposure to different Data architectures, data lake \& data warehouse
Define tools \& technologies to develop automated data pipelines, write ETL processes, develop dashboard \& report and create insights### Responsibilities
Technical Skills
Primary Skills: Python, SQL, Snowflake/Databricks, AWS (S3, Glue, EKS, Bedrock, Kinesis, Redshift), Docker, Kubernetes, Terraform, GitHub Actions, LangChain, LlamaIndex, LLM APIs (OpenAI, AWS Bedrock, Claude, HuggingFace), (Pinecone, FAISS, ChromaDB, OpenSearch), knowledge graphs (Neo4j).
Secondary Skills: MLflow, FastAPI, CI/CD pipelines, observability tooling (CloudWatch, Grafana, or equivalent), data lineage and metadata management platforms.
- 15\+ years of hands\-on data engineering and architecture experience, alongside building production AI/ML and LLM\-era data infrastructure.
- Strong Experience with either Databricks or Snowflake; experience with both is desirable.
- Strong data architecture patterns \& principles, ability to design secure \& scalable data lakes, data warehouse, data hubs, and other event\-driven architectures
- Expertise in designing and writing ETL processes in Python / Java / Scala
- Own the full data stack: real\-time streaming (Kafka, Spark Structured Streaming), batch processing (Databricks, PySpark, Delta Lake), cloud storage and compute (AWS, Azure), and data quality /metadata management.
- Drive modernisation of legacy pipelines (on\-prem ETL, batch DWH) to cloud\-native, AI\-ready architectures with measurable improvements in cost, latency, and delivery velocity.
- Proven experience designing enterprise\-scale AI data platforms that serve multiple AI consumers —not just one application or pipeline.
- Hands\-on experience with vector stores, semantic models, knowledge graphs, and retrieval infrastructure in production environments.
Working knowledge of LLMOps: model serving pipelines, MLflow, CI/CD for AI, automated evaluation, and production monitoring.
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### AI Experience
RAG, Vector \& Retrieval Infrastructure
Design the retrieval infrastructure that powers RAG\-based AI applications: embedding pipelines, vector stores (Pinecone, FAISS, ChromaDB, OpenSearch), chunking strategies, and hybrid retrieval layers combining semantic search with structured queries.
Agentic Behaviour Observability \& Output Accuracy
Own the observability stack for AI agent behaviour: instrument agents to capture inputs, retrieved context, tool calls, reasoning traces, and outputs — creating a complete audit trail of every agentic action driven by platform data.
Design and operate evaluation frameworks that continuously measure AI output quality: factual accuracy, context faithfulness, retrieval relevance, hallucination rates, and task completion success— across all AI consumers of the platform.
Architecture Standards \& Engineering Enablement
Define and maintain the reference architecture for the AI data platform — documenting design patterns, data contracts, integration standards, and decision records (ADRs) that all engineering teams follow.
Establish data engineering standards: pipeline testing frameworks, code review practices, CI/CD automation, infrastructure\-as\-code (Terraform), reusable component libraries, and observability instrumentation.### Benefits
- Medical Insurance benefits as per company policy.
- Dental insurance as per company policy.
- Vision insurance as per company policy.
- Employer paid Disability, Life, and AD\&D insurance
- Unlimited PTO
- Paid parental leave
- 401K
- Flexible work policy
- 12 Paid Holidays
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3Pillar Global, 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.
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.
3Pillar Global AI Hiring
3Pillar Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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