Senior AI Platform Engineer

Luxoft

New York (NY)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Job summary

Luxoft is seeking an AI Platform Engineer for AI & Emerging Tech in New York. You will drive enterprise AI platform enablement, partner with Security, Risk, Legal, and Compliance, and implement controls through configuration and code.

You will manage tokens, credits, and spend, while helping users adopt responsible AI practices. The role blends software engineering with GenAI platform experience, cloud-native delivery, and risk management, requiring collaboration across teams to enable AI

Qualifications

6+ years of progressive engineering experience, including 1-2+ years in AI platform, cloud platform, or emerging-tech enablement role
Experience with InfoSec, Risk, and Compliance to define, review, and implement technical controls in regulated environment
Hands-on experience implementing controls through configuration and code: IAM/access policies, guardrails, logging and audit trails, quota/rate limiting, and network/data-protection control
Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering technique
Agentic AI, MCP, and Graph/RAG architectures, including building and supporting agents, skills, and MCP server
Gen AI frameworks and LLM gateway/proxy pattern
AWS cloud services (AgentCore, Bedrock, EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation), including cost management and usage/credit monitoring
Infrastructure as Code (Terraform, Puppet, Docker) and containerized deployment
Python programming (NumPy, Pandas, Boto3) for automation, tooling, and platform service
Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4j, Neptune) and query optimization
Automated testing and evaluation frameworks (Ragas, Playwright, Selenium, Zephyr)
Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align)
Strong stakeholder management and ability to broker agreements across security, risk, compliance, and engineering team
Strong written and verbal communication, including translating technical controls into business language and vice versa
Customer-service mindset for user support, with the ability to triage, prioritize, and resolve technical issues efficiently

Responsibilities

Lead AI platform enablement workstreams, onboarding users/teams and use cases onto enterprise AI platforms (e.g. OpenAI, Gemini Anthropic, Amazon Bedrock, LLM gateways, and agentic frameworks)
Coordinate with Information Security, Risk, Compliance, Legal, and business teams to review, negotiate, and agree on platform controls and guardrails
Translate security, risk, and compliance requirements into technical controls via configuration changes or code (IAM policies, guardrails, content filters, logging/monitoring, rate limits)
Review and document controls, obtain signoffs, and maintain evidence for audit and compliance review
Ensure governance, DevSecOps protocols, and responsible AI standards across the platform
Manage token budgets, usage/credit limits, quotas, and rate limits; define allocation models balancing productivity and cost
Monitor platform costs and usage; build dashboards, alerting, and anomaly detection for spend issues
Provide day-to-day user support on credit/usage limits, quota management, and cost allocation
Advise users on AI usage guidance, approved patterns, and platform best practices
Support and troubleshoot questions related to skills, agents, MCP servers/integrations, prompts, and API usage
Create and maintain runbooks, FAQs, onboarding guides, and self-service documentation
Monitor operational metrics and incidents to drive reliability and platform adoption
Design, build, and maintain Gen AI platform capabilities including LLM pipelines, agentic workflows, MCP integrations, Graph/RAG using Python and AWS tooling
Implement cloud-native solutions using AWS services (EKS, Lambda, Fargate, Glue, Athena)
Automate platform provisioning, control enforcement, policy checks, and cost guardrails via IaC
Act as SME on Gen AI platform technologies and help shape the organization's AI platform roadmap
Own end-to-end delivery of platform enablement initiatives using Agile practices (Scrum/Kanban)

Job description

As an AI Platform Engineer for AI & Emerging Tech, you will drive AI platform enablement across the enterprise. This role sits at the intersection of engineering, governance, and user enablement: you will partner with Information Security, Risk, Legal and Compliance teams to define and agree on platform controls, implement those controls through configuration and code changes that make AI capabilities usable in a controlled enterprise environment. Provide hands‑on technical support to AI products and platform users. You will also manage the cost of enterprise AI — ensuring tokens, credits, and platform spend are managed effectively, efficiently, and transparently.

The ideal candidate combines strong software engineering fundamentals with practical experience in GenAI platforms, LLM application patterns, cloud-native delivery, and enterprise risk management. This person should be comfortable translating policy and control requirements into technical implementation, while also helping users understand responsible AI usage, credit limits, access patterns, skills, agents, MCP integrations, platform capabilities and cost-efficient consumption patterns

Responsibilitie

s:Lead AI platform enablement workstreams, enable upcoming AI features and products for the organization, onboarding users/teams and use cases onto enterprise AI platforms (e.g. OpenAI, Gemini Anthropic, Amazon Bedrock, LLM gateways, and agentic frameworks
).Coordinate with cross-functional stakeholders — Information Security, Risk, Compliance, Legal, and business teams — to review, negotiate, and agree on platform controls and guardrail
s.Translate agreed security, risk, and compliance requirements into technical controls, implemented via platform configuration changes or custom code (e.g., IAM policies, guardrails, content filters, logging/monitoring, rate limits, data-access controls). Work with cross engineering teams to enable these control
s.Review and document controls, obtain signoffs, and maintain evidence for audit and compliance review
s.Ensure adherence to enterprise governance, DevSecOps protocols, and responsible AI standards across the platfor
m.Manage token budgets, usage/credit limits, quotas, and rate limits across providers and teams; define allocation models that balance user productivity with cost disciplin
e.Proactively monitor AI platform costs and usage; build dashboards, anomaly detection, and automated alerting to notify users and teams of unusual spend, usage spikes, or quota breaches before they become budget issue

s.User Support & Enableme

ntProvide day-to-day user support on credit/usage limits, quota management, and cost allocation for AI platform consumptio
n.Advise users on AI usage guidance, approved patterns, and platform best practice
s.Support and troubleshoot technical questions related to skills, agents, MCP (Model Context Protocol) servers/integrations, prompt-based applications, and API usag
e.Create and maintain runbooks, FAQs, onboarding guides, and self-service documentation to scale suppor
t.Monitor operational metrics, usage, and incident data to drive continuous improvement, reliability, and platform adoptio
ryDesign, build, and maintain scalable Gen AI platform capabilities, including LLM pipelines, agentic workflows, MCP integrations, and Graph/RAG architectures, using clean, maintainable Python and AWS-native toolin
g.Implement cloud-native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athen
a.Automate platform provisioning, control enforcement, policy checks, and cost guardrails (budgets, alerts, quota enforcement) using Infrastructure as Cod
e.Act as a subject matter expert (SME) on Gen AI platform technologies and help shape the organization's AI platform roadma
p.Own end-to-end delivery of platform enablement initiatives; manage timelines, deliverables, and milestones using Agile practices (Scrum/Kanban

).Mandatory Skills Descriptio

n:6+ years of progressive engineering experience, including 1-2+ years in AI platform, cloud platform, or emerging-tech enablement role
s.Demonstrated experience working with InfoSec, Risk, and Compliance teams to define, review, and implement technical controls in regulated environment
s.Hands-on experience implementing controls through configuration and code: IAM/access policies, guardrails, logging and audit trails, quota/rate limiting, and network/data-protection control
s.Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering technique
s.Agentic AI, MCP, and Graph/RAG architectures, including building and supporting agents, skills, and MCP server
s.Gen AI frameworks and LLM gateway/proxy pattern
s.AWS cloud services (AgentCore, Bedrock, EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation), including cost management and usage/credit monitorin
g.Infrastructure as Code (Terraform, Puppet, Docker) and containerized deployment
s.Python programming (NumPy, Pandas, Boto3) for automation, tooling, and platform service
s.Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4j, Neptune) and query optimizatio
n.Automated testing and evaluation frameworks (Ragas, Playwright, Selenium, Zephyr
).Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align
.Strong stakeholder management and ability to broker agreements across security, risk, compliance, and engineering team
s.Clear written and verbal communication, including translating technical controls into business language and vice vers
a.Customer-service mindset for user support, with the ability to triage, prioritize, and resolve technical issues efficientl

y.Nice-to-Have Skills Descriptio