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About This Role
Role: Senior AI Platform Engineer
Location: Remote
Duration: Long term contract
About the Role :
We are looking for a Senior AI Platform Engineer to build the foundational infrastructure required to securely deploy, govern, observe, and scale enterprise AI capabilities. The role will start with a focused 3–6 month mandate to enable coding agents, including GitHub Copilot and Claude Code using BYOK deployment patterns, with model access served through Parsons\-controlled cloud environments across Azure AI Foundry and AWS Bedrock.
In parallel, you will extend that same foundation into a broader AI platform capability for enterprise AI applications that require governed model access, MCP\-based access to tools and data, secure agent runtime patterns, and reusable agent development platforms in Azure AI Foundry and AWS AgentCore.
Phased Mandate: Phase 1 focuses on standing up the secure AI enablement foundation for coding agents. Phase 2 extends the same platform patterns to broader AI applications, enterprise MCP access, and agent development platforms in Azure AI Foundry and AWS AgentCore.
What You Will Do: Phased RoadmapPhase 1: Build the Coding\-Agent Foundation, First 3–6 Months
· Deploy governed BYOK patterns: implement reusable infrastructure patterns for GitHub Copilot and Claude Code where model calls are routed through Parsons\-controlled cloud tenancy rather than unmanaged vendor defaults.
· Administer GitHub Copilot SaaS where required: govern GitHub Copilot SaaS administration for users who remain on the standard SaaS model while most users are enabled through approved BYOK patterns, including license assignment, access policies, usage visibility, policy configuration, and coordination with GitHub/M365 administrators.
· Integrate Azure AI Foundry and AWS Bedrock: support approved model hosting, model routing, model access policies, and secure connectivity across Azure and AWS government/commercial boundary patterns as approved by architecture, security, and compliance.
· Create reusable onboarding patterns: build infrastructure templates, sample repositories, deployment pipelines, and setup guides so development teams can adopt approved coding\-agent capabilities quickly, consistently, and securely.
· Design for regulated developer environments: support secure developer workstations, GovCloud/GCC High constraints, private networking, identity federation, secrets management, and least\-privilege access patterns.
· Create the bridge to broader AI platform reuse: design the coding\-agent foundation as reusable enterprise infrastructure, not a one\-off implementation, so model access, policy, observability, and MCP patterns can be extended to other AI applications.
2\. Establish AI Gateway and MCP Gateway Capabilities
· Azure AI Gateway / APIM: design and operate gateway controls for model routing, authentication, authorization, model allow\-lists, token budgets, rate limits, circuit breakers, content filtering, and usage attribution.
· Enterprise MCP Gateway: build a secure broker layer that allows coding agents to access enterprise data, APIs, developer tools, repositories, ticketing systems, and approved SaaS services without exposing direct unmanaged connections.
· Connector governance: define onboarding, registration, approval, versioning, and decommissioning patterns for MCP servers, APIs, tools, and data connectors.
· Trust\-boundary enforcement: make data classification, approved data types, tenant boundaries, cross\-cloud routing, and egress controls explicit and auditable.
3\. Implement Governance, Security, and Compliance Controls
· Policy enforcement: implement technical controls for model eligibility, developer access, approved use cases, prompt/response handling, data loss prevention, and tool\-call authorization.
· Compliance alignment: partner with Security, Legal, Risk, and Federal stakeholders to design controls aligned to NIST 800\-53, NIST 800\-171, CMMC, FedRAMP, FISMA, and DoD impact\-level requirements where applicable.
· CUI\-aware architecture: design patterns that support Controlled Unclassified Information handling requirements, including tenant selection, authorized model endpoints, encryption, audit logging, and controlled egress.
· Human oversight: implement review, approval, exception, escalation, and rollback workflows for high\-risk coding\-agent use cases and sensitive integrations.
4\. Build Observability, AgentOps, and FinOps
· Unified telemetry: capture model calls, prompts/responses metadata, tool calls, data access, latency, errors, cost, token usage, policy decisions, and user/team attribution.
· Common AI FinOps dashboard: build shared reporting for token consumption, model usage, API calls, cost attribution, budget thresholds, anomaly detection, adoption trends, forecast vs. actual spend, and showback/chargeback across teams, applications, coding agents, and enterprise AI agents.
· Evaluation and quality controls: define test harnesses, regression evaluations, red\-team scenarios, hallucination/failure detection, and release gates for coding\-agent workflows.
· Production readiness: establish SLOs, runbooks, alerting, incident response, root\-cause analysis, and continuous hardening practices for AI gateway and MCP gateway services.
5\. Enable Developers and Platform Adoption
· Developer onboarding: create clear onboarding guides, sample projects, secure defaults, approved model\-selection guidance, and training materials for development teams.
· Reusable engineering patterns: publish reference architectures for coding\-agent workflows, secure prompt/data handling, repository access, PR/code\-review workflows, and tool\-use guardrails.
· Cross\-functional delivery: work with Enterprise Architecture, Cloud Engineering, Security, SRE, Developer Productivity, Legal/Risk, and business\-unit engineering teams.
· Broader AI application enablement: support product and engineering teams that need governed access to foundation models, enterprise tools, APIs, data sources, and agentic workflows beyond software development use cases.
· Standards leadership: set engineering standards for agentic code development, BYOK usage, MCP design, gateway policy, and observability across AI engineering teams.
Phase 2: Extend to Enterprise AI Applications and Agent Platforms
· Agent development platforms: build and operate reusable agent development platforms in Azure AI Foundry and AWS AgentCore, including secure templates, deployment pipelines, runtime patterns, testing/evaluation workflows, and operating standards.
· Model\-access foundation: provide governed model access for AI applications through approved gateways, policy enforcement, usage metering, tenant isolation, data classification controls, and cost\-management patterns.
· MCP\-enabled enterprise integration: enable AI applications and agents to access approved enterprise systems through governed MCP patterns, including tool registration, access approval, logging, decommissioning, and trust\-boundary enforcement.
· Platform roadmap ownership: partner with Enterprise Architecture, Security, Cloud, Data, and application teams to evolve the platform from coding\-agent enablement into a shared enterprise AI application foundation.
Required Qualifications
· 8\+ years of software, cloud, platform, DevOps, or infrastructure engineering experience, including 3\+ years building enterprise cloud/platform services and 1–2\+ years supporting AI/ML, LLM, or agentic systems.
· Strong hands\-on engineering skills with Python plus infrastructure\-as\-code experience using Terraform, Bicep, CloudFormation, or equivalent.
· Experience deploying cloud\-native services using containers, Kubernetes or serverless patterns, CI/CD, secrets management, private networking, and enterprise identity controls.
· Hands\-on experience with Azure AI Foundry, Azure API Management, Azure Monitor/Application Insights, Microsoft Entra ID, Key Vault, Private Link, Azure Policy, and related governance/security services.
· Working knowledge of AWS Bedrock, AWS GovCloud, IAM, CloudWatch, networking, and secure cross\-cloud or multi\-cloud operating patterns.
· Experience designing or operating LLM gateways, model\-routing layers, API gateways, MCP servers/gateways, tool/function calling, RAG pipelines, or agent orchestration frameworks.
· Experience administering or governing GitHub Copilot SaaS, including access management, policy configuration, licensing, usage reporting, and coordination with GitHub or Microsoft 365 platform administration teams.
· Strong understanding of security architecture: least privilege, RBAC/ABAC, conditional access, encryption, egress controls, audit logging, data classification, and secrets management.
· Ability to design controls for regulated environments, including NIST, CMMC, FedRAMP, FISMA, and CUI handling requirements.
· Experience building observability for distributed systems, including logs, metrics, traces, dashboards, alerting, SLOs, incident response, and operational runbooks.
· Excellent communication skills with the ability to explain architecture, risk, cost, compliance, and operational trade\-offs to technical and non\-technical stakeholders.
Preferred Qualifications
· Experience with GitHub Copilot Enterprise, GitHub Copilot BYOK, Claude Code, Anthropic SDKs, OpenAI/Azure OpenAI APIs, AWS Bedrock model access, or comparable coding\-agent platforms.
· Experience with MCP, A2A, LangGraph, Semantic Kernel, LlamaIndex, LangChain, AgentCore, or related orchestration/tooling frameworks.
· Experience building AI gateways with model allow\-lists, content safety, prompt\-injection defenses, usage metering, model routing, rate limits, circuit breakers, and cost attribution.
· Experience with Microsoft Purview, Defender for Cloud, Sentinel/SIEM integration, policy\-as\-code, OpenTelemetry, Langfuse, Arize, LangSmith, or other AI observability/evaluation tools.
· Experience supporting Defense Industrial Base, Federal, government cloud, GCC High, Azure Government, AWS GovCloud, or environments processing CUI.
· Experience implementing chargeback/showback models for cloud, token, model, API, or developer\-platform usage.
· Experience with AI FinOps, cloud cost management, tokenomics, cost allocation, budget guardrails, consumption forecasting, anomaly detection, or showback/chargeback reporting for AI platforms.
· Experience defining product/platform roadmaps, operating models, onboarding processes, SLOs, runbooks, and stakeholder communication for shared enterprise platforms.
Preferred Certifications
· Microsoft Certified: Azure Solutions Architect Expert, Azure AI Engineer Associate, Azure Security Engineer Associate, or comparable Azure certification.
· AWS Certified Solutions Architect, AWS Security Specialty, AWS Machine Learning, or comparable AWS certification.
· GitHub Copilot, GitHub Actions, GitHub Advanced Security, or related GitHub platform certification or demonstrated equivalent experience.
· FinOps Certified Practitioner, Terraform/Kubernetes certification, CISSP, CCSP, CompTIA Security\+, or comparable cloud, platform, FinOps, or security certification.
Pay: $80\.00 \- $90\.00 per hour
Work Location: Remote
Salary Context
This $166K-$187K range is above the median 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 Acuhuman, 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 ($176K) sits 18% below the category median. Disclosed range: $166K to $187K.
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.
Acuhuman AI Hiring
Acuhuman has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $187K - $187K.
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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