Forward Deployed AI Architect

$170K - $230K Remote Mid Level AI Architect

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Skills & Technologies

Rag

About This Role

AI job market dashboard showing open roles by category

Most enterprise AI initiatives stall between pilot and production. The demo works; the path to a system that runs a real business process, under real controls, with a real owner, is where they get stuck. The Forward Deployed AI Architect is the person we place inside the client to get them unstuck.

You will be embedded with client business owners and engineering leaders, designing AI-native, Enterprise AI and Applied AI solutions — assistants, RAG systems, agents, agentic workflows, autonomous components — and taking them from pilot to production. You will carry the architect's responsibilities (framing the problem, fitting the business scope exactly, making trade-offs explicit, deciding what the system must not do) while sitting at the client's table, in their vocabulary, on their timeline.

The role is modeled on the forward-deployed engineering pattern but operates at the architecture level. It rewards people who like ownership, who can work with ambiguity without a home team beside them, and who measure themselves by what reached production at the client, not by what was presented.

Req# 1090267686

Responsibilities

  • Embed with a client account, working directly with business owners, product leaders, engineering teams, security, risk, and operations to take AI solutions from pilot to production
  • Design end-to-end AI-native architectures: orchestration, retrieval, integration with systems of record, agent autonomy and guardrails, identity and authorization, evaluation, observability, and cost model
  • Own three artifacts that make your impact measurable: a capability economics model — what each AI capability costs and what it earns, so investment decisions are made on evidence; trust-rung evidence for every agent — the documented proof of how much autonomy each agent has earned and under what controls; a knowledge-graph slice of the client domain — the entities, relationships, and constraints the solution depends on, captured so the architecture and the business share one model
  • Turn contradictory or incomplete requirements into explicit business decisions with named owners
  • Decide, per component and per risk, how much can be delegated to AI — including AI coding agents in the delivery process — and where a human must stay in the loop
  • Define how each system is evaluated: how the client will know an assistant or agent fits the business scope, not just that it produces fluent output
  • Build proofs of concept and reference implementations when a decision needs evidence or a team needs an example of "good"
  • Feed what you learn at the client back into EPAM's AI architecture practice: patterns, reference architectures, and what generation gets wrong

Requirements

  • You have taken AI-native systems to production at a client or customer — RAG, assistants, agents or agentic workflows with real users and real business processes behind them — in a solutions architecture, customer engineering, forward-deployed, or consulting role, with architect-level responsibility for the system around the model
  • You are a proven solution architect from a consulting or client-facing background who has started building with AI on your own initiative in the last year or so, and wants to make AI-native design at the client's side the center of your work
  • A track record of client- or customer-facing architecture: you have framed problems for people who are not technologists, presented options with costs and risks, and got decisions made
  • Solid solution or software architecture experience with delivery accountability — systems you designed that shipped and ran
  • Depth in integration, distributed systems, cloud, and data-flow architecture at enterprise scale
  • Comfort operating with ambiguity, conflicting stakeholders, and shifting requirements, without waiting for clarity before starting
  • A habit of writing things down so decisions survive you: ADRs, design documents, evidence for autonomy and risk decisions
  • Recent hands-on coding ability, enough to prototype and keep your constraints honest
  • Willingness to be on-site with clients and to travel as the engagement requires

Nice to have

  • Regulated-industry experience — financial services, healthcare and life sciences, insurance — including working with security, risk, and compliance functions
  • Agent frameworks, tool and function calling, MCP, orchestration patterns, LLM observability
  • Identity and authorization for agentic systems (OAuth 2.1, SPIFFE, AuthZEN, DPoP)
  • Experience with cost and token economics, or with building business cases for AI capabilities
  • Presales, proposal, or account-shaping experience
  • Forward-deployed or field engineering background at an AI or data platform vendor

We offer

  • Medical, Dental and Vision Insurance (Subsidized)
  • Health Savings Account
  • Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
  • Short-Term and Long-Term Disability (Company Provided)
  • Life and AD&D Insurance (Company Provided)
  • Employee Assistance Program
  • Unlimited access to LinkedIn learning solutions
  • Matched 401(k) Retirement Savings Plan
  • Paid Time Off – the employee will be eligible to accrue 15-25 paid days, depending on specific level and tenure with EPAM (accrual eligibility may change over time)
  • Paid Holidays - nine (9) total per year
  • Legal Plan and Identity Theft Protection
  • Accident Insurance
  • Employee Discounts
  • Pet Insurance
  • Employee Stock Purchase Program
  • If otherwise eligible, participation in the discretionary annual bonus program
  • If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program

For remote work in New York City only.

EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our clients, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.

Engineer the Future with a Career at EPAM

This posting includes a good faith range of the salary EPAM would reasonably expect to pay the selected candidate. The range provided reflects base salary only. Individual compensation offers within the range are based on a variety of factors, including, but not limited to: geographic location, experience, credentials, education, training; the demand for the role; and overall business and labor market considerations. Most candidates are hired at a salary within the range disclosed. Salary range: $170,000 - $230,000. In addition, the details highlighted in this job posting above are a general description of all other expected benefits and compensation for the position.

Applications will be accepted on a rolling basis.

EPAM will not provide new H-1B visa sponsorship for this position. Candidates with existing transferable H-1B status may be considered.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Salary Context

This $170K-$230K range is above the median for AI Architect roles in our dataset (median: $179K across 32 roles with salary data).

Role Details

Company EPAM Systems
Title Forward Deployed AI Architect
Location Remote, US
Category AI Architect
Experience Mid Level
Salary $170K - $230K
Remote Yes

About This Role

This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.

The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.

Across the 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At EPAM Systems, this role fits into their broader AI and engineering organization.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

What the Work Looks Like

Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

Skills Required

Rag (7% of roles)

Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.

Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.

Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

Compensation Benchmarks

AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($200K) sits 13% below the category median. Disclosed range: $170K to $230K.

Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.

EPAM Systems AI Hiring

EPAM Systems has 14 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Consultant, AI Software Engineer. Positions span Remote, US, Houston, TX, US, New York, NY, US. Compensation range: $90K - $260K.

Remote Work Context

Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.

Career Path

Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.

From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.

Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.

What to Expect in Interviews

AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.

When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

AI Hiring Overview

The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.

The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

The AI Job Market Today

The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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

Based on 212 roles with disclosed compensation, the median salary for AI Architect positions is $230,000. Actual compensation varies by seniority, location, and company stage.
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
About 6% of the 9,653 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
EPAM Systems is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Architect positions include Senior Engineer, AI Architect, Engineering Manager, Principal Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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