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About This Role
Purpose
We’re looking for a backend\-leaning, Senior, Full Stack Engineer who will build AI\-powered platforms, tools, and workflows that create value for our clients and empower our creative, strategy, operations, and account teams.
You’ll design and build backend services, data\-centric components, and internal tools, with a strong focus on Python and modern cloud infrastructure. You will be hands\-on with integrating large language models (LLMs) and other AI capabilities into real products, from early design through deployment, monitoring, and iteration.
Ideal Candidate
- You’re a strong backend\-focused engineer who thinks in terms of systems, data models, and APIs.
- You’re comfortable hopping into simple frontend tasks when needed.
- Enjoys collaborating closely with cross\-functional partners.
- You can translate requirements into scalable software that balances speed, quality, and reliability.
- You’re curious about AI and other emerging technology and excited to integrate them responsibly into real products.
- You take ownership of products, from design through deployment and maintenance.
Responsibilities
- Design, build and maintain backend services and APIs primarily in Python (FastAPI/Starlette), emphasizing clean design, performance, and reliability.
- Model data and write high‑quality SQL (primarily in BigQuery); use document databases (e.g., Firestore, MongoDB) where appropriate.
- Build, harden, and operate containerized services: author Dockerfiles (multi‑stage), manage image versions in Artifact Registry, and enforce container security/scanning.
- Deploy on GCP with Cloud Run and Compute Engine; leverage Secret Manager, Artifact Registry, Cloud Build/Deploy, and Cloud Monitoring/Logging; Kubernetes familiarity is a plus.
- Integrate LLM/AI capabilities with an agentic approach (tool/function calling, multi‑step orchestration/planning, retrieval/RAG, and memory) using providers such as OpenAI, Anthropic, and Google Gemini, as well as open‑weight models; implement evaluation, safety, and guardrails.
- Utilize our enterprise AI platform (Abacus.ai) that provides unified access to multiple language, image, and short‑form video models, plus prompt/version management, safety, and analytics; help define reusable patterns and abstractions for it across products.
- Collaborate with data partners on ELT pipelines; use BigQuery and Dataform for transformations and analytics use cases.
- Define and version API contracts (REST/GraphQL); document systems and interfaces.
- Apply security and privacy best practices (authn/z, IAM least‑privilege, secret handling, input validation, rate limiting).
- Establish observability (metrics, logs, traces) and conduct performance tuning; participate in pragmatic on‑call as needed.
- Write tests (unit/integration/e2e); maintain CI/CD pipelines; conduct code reviews; mentor junior engineers
Professional Skills
- Strong experience building backend services and APIs in Python (any modern web framework)
- Experience with document databases (e.g., Firestore, MongoDB).
- Containers \& CI/CD: Docker/OCI image authoring, multi‑stage builds, image scanning/SBOMs, Artifact Registry; automated builds and deployments.
- Cloud: GCP first (Cloud Run and Compute Engine; Secret Manager, Artifact Registry, Cloud Build/Deploy, Monitoring/Logging); Kubernetes familiarity welcome; equivalent AWS/Azure experience acceptable.
- AI/LLM: Agentic architectures (tool/function use, multi‑step orchestration, retrieval/RAG, planners, memory), evaluation/guardrails/safety; experience with OpenAI, Anthropic, Google Gemini, and open‑weight models; familiarity with enterprise AI platforms that unify access to multiple model types.
- APIs \& Services: REST/GraphQL, schema/versioning, authentication/authorization.
- Reliability: Testing (Pytest or similar), observability, performance tuning.
- Frontend: Able to handle simple UI needs using modern web technologies; framework agnostic.
- Process: Git‑based workflows and agile practices.
Competencies
- Communicates and collaborates effectively with creative, operations, strategy, and data partners.
- Outcome‑oriented problem solving; balances speed, quality, and security.
- Ownership and accountability; follows through and documents decisions.
- Growth mindset; receptive to feedback and continuous learning.
- Uses AI assistants responsibly with validation: evaluates outputs critically, adds tests, and adapts code to team conventions before submission.
Experience
- 4\+ years of professional software engineering with a backend focus.
- Proven and demonstrable experience building Python (FastAPI/Starlette) services and APIs for cloud deployment (GCP preferred).
- Hands\-on SQL experience in BigQuery; document database experience; Dataform exposure is a plus.
- Prior experience integrating LLMs in an agentic manner into production apps or adjacent ML systems.
Salary Range
Our estimated range for this role is $140k \- $160k
Compensation packages are based on the skill level and experience each candidate brings to their role. There may also be a more senior or junior position available that could be a better fit with your expertise. Each level has its own compensation range.
We pride ourselves on competitive salaries, and ensuring pay equity exists across our organization. We benchmark each position against existing employee competencies and 4As compensation data which includes geographic and agency size benchmarks. We also meet with department leaders 3x/year to ensure we are supporting employees in living into their full potential. Our promotions are not limited to a specific time per year. Promotions are tied to performance.
Right To Work In The US
You must be authorized to work in the US for any employer. At this time, we are not sponsoring or providing assistance with obtaining work authorization.
McKinney is a place where everyone can grow. Studies have shown that marginalized communities such as women, LGBTQ\+ and people of color are less likely to apply to jobs unless they meet every single qualification. However you identify, and whatever background you bring with you, please apply if this is a role that would make you excited to come into work every day.
We are in the office Tuesday/Wednesday/Thursday on a hybrid schedule. We look forward to meeting you!
About McKinney
McKinney is a creative agency that gets unfair attention for brands. In 2024, McKinney was named to Fast Company's Best Workplaces for Innovators list, as well as Ad Age's A\-List and its list of Best Places to Work (2024 and 2025\), reinforcing the agency's commitment to providing an exceptional workplace culture where employees thrive, and creativity flourishes. McKinney Health, the agency's Pharma and Wellness practice, launched in 2022, was named to MM\+M Magazine's 2024 Agency 100 list. A Certified B Corporation, McKinney is part of the Cheil Worldwide network and has offices across the country, including Durham, New York, Los Angeles, Dallas, Phoenix, and Toronto. McKinney has been recognized by Cannes Lions, Effies, The One Show, D\&AD, ANDY, CLIO, LIA, the Shortys, and The Webby Awards, among others. Client partners include brands such as Popeyes, Blue Diamond Growers, Little Caesars, Pampers, Henkel, Samsung, Indivior, Sherwin\-Williams, Biogen and the Ad Council. For more information, visit mckinney.com.
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At McKinney, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
McKinney AI Hiring
McKinney has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $160K - $160K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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