Careers
We're scaling to serve the world's most ambitious companies.
Phil Maher's AI consulting practice is growing from a solo operation into a team that can take on enterprise-scale engagements — the kind where a single implementation can save a Fortune 500 company millions per year. We need exceptional people who can operate at that level.
How we work
- •We build production systems, not slide decks. Everyone here ships.
- •We work directly with C-suite executives and operators — no layers of abstraction.
- •We use AI in our own workflow every day. If you're not already building with LLMs, you're behind.
- •Quality over quantity. We'd rather do 5 transformative engagements a year than 50 mediocre ones.
- •Remote-first, async-heavy, outcome-measured. No performative meetings.
Open positions
Every role below exists because it directly unblocks our ability to take on larger, more complex enterprise engagements. If you see yourself in one of these descriptions, we should talk.
Senior AI Solutions Architect
Lead the technical design of enterprise AI implementations from discovery through production deployment. You'll be the senior technical mind in the room with CTOs and VPs of Engineering at large companies — the person they trust to make the right architecture calls.
Why this role matters
This is the highest-leverage hire we can make. Enterprise clients buy confidence in technical leadership. To take on $500K+ engagements with Fortune 500 companies, we need architects who've operated at that scale and can own the full technical relationship.
What you'll do
- •Lead technical discovery and architecture design for enterprise AI engagements
- •Design RAG systems, agent workflows, document processing pipelines, and ML infrastructure at scale
- •Make build vs. buy decisions across the AI stack — models, infrastructure, tooling, and vendors
- •Produce architecture documents, cost models, and implementation roadmaps for C-suite audiences
- •Own the technical relationship with enterprise clients from kickoff through handoff
- •Evaluate and select between open-source and commercial AI models based on client constraints
- •Design for enterprise requirements: SOC 2, HIPAA, data residency, SSO, audit logging
- •Mentor junior engineers and establish technical standards across engagements
What we're looking for
- •10+ years in software architecture, with 3+ years focused on AI/ML systems
- •Hands-on production experience with LLMs, RAG systems, and AI infrastructure
- •Deep understanding of enterprise security, compliance, and deployment patterns
- •Proven ability to communicate technical tradeoffs to non-technical executives
- •Experience with Rust, Python, or TypeScript in production systems
- •Track record of leading technical engagements with large organizations ($50M+ revenue)
- •Strong opinions on AI architecture, loosely held — grounded in real shipping experience
Bonus points
- •Experience at a top-tier consulting firm (McKinsey Digital, Thoughtworks, Palantir, etc.)
- •Background in financial services, legal tech, or healthcare AI
- •Open-source contributions to AI/ML tooling
- •Experience with self-hosted model deployment (vLLM, TGI, Ollama at scale)
Enterprise Business Development Lead
Build and run the pipeline that turns Fortune 500 interest into signed enterprise engagements. You'll be the first dedicated business hire — shaping how we position, price, and close deals with the world's largest companies.
Why this role matters
Technical excellence means nothing without a pipeline. Scaling from inbound consulting leads to proactive enterprise sales requires someone who understands how large companies buy AI services — the procurement cycles, the stakeholder mapping, the trust-building. This role directly determines our growth trajectory.
What you'll do
- •Identify and qualify enterprise prospects with high-value AI implementation needs
- •Build relationships with C-suite, VP, and Director-level buyers at target accounts
- •Develop proposals, SOWs, and pricing strategies for $200K–$2M+ engagements
- •Navigate enterprise procurement, legal review, and vendor qualification processes
- •Partner with the technical team to scope engagements accurately and competitively
- •Build repeatable sales processes, collateral, and case studies for enterprise positioning
- •Represent the practice at industry events, executive roundtables, and conferences
- •Own pipeline metrics: qualified leads, proposal win rate, average deal size, time-to-close
What we're looking for
- •7+ years in enterprise sales or business development in technology consulting or professional services
- •Proven track record closing $500K+ services deals with large organizations
- •Deep understanding of how enterprises evaluate and purchase AI/ML consulting services
- •Ability to translate technical capabilities into business value for executive buyers
- •Experience with consultative selling — discovery-led, not pitch-led
- •Strong network in industries where AI implementation has the highest ROI (financial services, professional services, operations-heavy enterprises)
- •Comfortable working in a small, fast-moving team where you build the playbook as you go
Bonus points
- •Existing relationships at Fortune 500 companies exploring AI adoption
- •Background selling AI/ML, data engineering, or cloud infrastructure services
- •Experience at firms like Accenture, Deloitte Digital, Slalom, or similar consultancies
- •Understanding of AI technology at a level where you can hold your own in technical conversations
Senior AI/ML Engineer
Build the production AI systems our clients depend on. You'll implement RAG pipelines, fine-tuning workflows, agent systems, and data processing infrastructure — writing the code that turns architecture designs into running, reliable software.
Why this role matters
We can't scale to multiple concurrent enterprise engagements with a single builder. We need engineers who can take an architecture spec and ship production-grade AI systems independently — the kind of people who write Rust backends that handle 70,000 backtests per second and deploy ML models to colocation servers.
What you'll do
- •Build production AI systems: RAG pipelines, agent workflows, document processing, classification engines
- •Implement and optimize LLM integrations (API-based and self-hosted open-source models)
- •Design and build data pipelines for ingestion, transformation, and vector embedding
- •Write performant, well-tested code in Rust, Python, and/or TypeScript depending on the engagement
- •Deploy and operate AI infrastructure: model serving, vector databases, monitoring, and observability
- •Collaborate with Solutions Architects to refine designs during implementation
- •Build internal tooling and frameworks that accelerate delivery across engagements
- •Participate in technical discovery and architecture reviews with clients
What we're looking for
- •5+ years in software engineering with 2+ years building AI/ML systems in production
- •Strong coding ability in at least two of: Rust, Python, TypeScript
- •Production experience with LLM APIs (OpenAI, Anthropic, etc.) and/or self-hosted models
- •Hands-on experience with RAG systems, vector databases (Pinecone, Qdrant, pgvector), and embedding pipelines
- •Solid understanding of ML fundamentals: evaluation, fine-tuning, prompt engineering, retrieval optimization
- •Experience deploying to cloud infrastructure (AWS, GCP, or Azure) with containers and CI/CD
- •Ability to work autonomously across concurrent client engagements
Bonus points
- •Experience with Rust for performance-critical systems (like our Ferrix trading engine)
- •Background in distributed systems or high-performance computing
- •Contributions to open-source AI/ML projects
- •Experience with enterprise integration patterns (SAML/SSO, audit logging, data residency)
- •Familiarity with financial services, legal, or healthcare compliance requirements
Technical Engagement Manager
Run complex, multi-workstream AI implementation engagements for enterprise clients. You're the person who keeps $500K+ projects on track, manages stakeholder expectations across organizations, and ensures we deliver exceptional results on time.
Why this role matters
Enterprise engagements fail on project management more often than on technology. When a Fortune 500 client has 12 stakeholders across 4 departments, someone needs to orchestrate the engagement with the same rigor we bring to the code. This role is what separates a consulting practice from a freelancer.
What you'll do
- •Own end-to-end delivery of enterprise AI implementation engagements
- •Manage scope, timeline, budget, and risk across multi-month, multi-workstream projects
- •Serve as the primary client relationship point for day-to-day engagement operations
- •Coordinate between internal technical teams and client stakeholders at all levels
- •Run structured discovery workshops, sprint reviews, and stakeholder updates
- •Identify scope creep early and manage change requests professionally
- •Build and maintain project documentation, status reports, and executive dashboards
- •Develop repeatable delivery frameworks and playbooks for common engagement types
What we're looking for
- •7+ years in technical project/engagement management at a consulting firm or professional services organization
- •Proven experience managing $250K+ technology implementation projects for enterprise clients
- •Strong understanding of AI/ML project lifecycles — discovery, data, development, deployment, adoption
- •Ability to manage ambiguity and translate vague client requirements into actionable project plans
- •Excellent stakeholder management skills across technical and executive audiences
- •Experience with SOW development, change order management, and margin oversight
- •Comfortable with both Agile delivery and the structured reporting enterprise clients expect
Bonus points
- •PMP, Scrum Master, or equivalent certification
- •Background at firms like Deloitte, Accenture, Thoughtworks, Pivotal, or similar
- •Technical depth sufficient to review architecture decisions and identify delivery risks
- •Experience in regulated industries (financial services, healthcare, government)
- •Track record of growing accounts — turning initial engagements into long-term partnerships
Platform & Infrastructure Engineer
Build and maintain the secure, scalable infrastructure that enterprise AI systems run on. You'll design deployment architectures that satisfy Fortune 500 security teams, set up model serving infrastructure, and ensure our client deployments are production-grade from day one.
Why this role matters
Enterprise clients don't just buy working AI — they buy infrastructure they can trust. SOC 2 compliance, data residency, private model hosting, zero-trust networking, audit trails. Every large deal we close will require an infrastructure story that satisfies a CISO. This role makes enterprise-scale delivery possible.
What you'll do
- •Design and implement secure deployment architectures for enterprise AI systems
- •Build infrastructure for self-hosted model serving (vLLM, TGI, Triton) on client or cloud infrastructure
- •Implement enterprise security requirements: SSO/SAML, RBAC, encryption at rest/in transit, audit logging
- •Create reproducible infrastructure-as-code (Terraform, Pulumi) for rapid client deployment
- •Set up monitoring, alerting, and observability for production AI workloads (Prometheus, Grafana, structured logging)
- •Manage GPU infrastructure for model training and inference — cost optimization and scaling
- •Build CI/CD pipelines for ML model deployment and AI application releases
- •Support SOC 2, HIPAA, and other compliance requirements across client engagements
What we're looking for
- •5+ years in infrastructure/DevOps/platform engineering
- •Deep experience with AWS, GCP, or Azure — networking, IAM, compute, storage, and GPU instances
- •Hands-on experience deploying and operating ML model serving infrastructure
- •Strong knowledge of container orchestration (Kubernetes, ECS) and infrastructure-as-code
- •Understanding of enterprise security: zero-trust architecture, secrets management, compliance frameworks
- •Experience with observability stacks: Prometheus, Grafana, ELK, or Datadog
- •Scripting and automation skills in Python, Bash, and/or Go
- •Comfort working across diverse client environments and technology stacks
Bonus points
- •Experience with GPU cluster management and ML infrastructure optimization
- •AWS Solutions Architect Professional or equivalent cloud certification
- •Background in regulated industries with strict data handling requirements
- •Experience with Rust-based infrastructure tooling
- •Contributions to open-source infrastructure or ML serving projects
Don't see the right fit?
We're always interested in hearing from exceptional people. If you're an AI builder, enterprise operator, or technical leader who wants to do the best work of your career — reach out.