Search Jobs | Dice.com

1110 results (73 new)

Group by company

Senior AI Platform Engineer - Nearshore

Diligente Technologies

Hybrid in Toronto, Ontario • 27d ago

Easy Apply

Contract

Depends on Experience

AI Platform Engineer

GlobalLogic Inc.

Toronto, Ontario • Yesterday

Easy Apply

Full-time, Third Party

Depends on Experience

Senior AI Platform Engineer (Automation & AIOps)

Randstad Digital

Remote or Richmond, Virginia • Today

Full-time

USD80 - USD110

AI Platform Engineer

The Intersect Group

Remote • Today

Easy Apply

Contract

Depends on Experience

AI Platform Engineer

International Rescue Committee

Remote or New York, New York • Today

Full-time

USD 88,277.00 - 102,451.00 per year

Principal AI Platform Engineer

M&T BANK CORPORATION

Remote • Today

Full-time

USD 139,700.00 - 232,900.00 per year

Senior AI Platform Engineer

Blackbaud

Remote • Today

Full-time

USD 117,200.00 - 157,500.00 per year

AI Platform Engineer -Agentic AI

TekAssembly

Remote • 28d ago

Easy Apply

Contract

Depends on Experience

Staff AI Platform Engineer

Form Energy

Remote or Somerville, Massachusetts • Today

Full-time

USD 128,660.00 - 184,966.00 per year

ServiceNow AI Platform Engineer (Remote)

Rivago infotech inc

Remote • 5d ago

Easy Apply

Contract, Third Party

60 - 70

Staff AI Platform Engineer

Sysdig

Remote or Raleigh, North Carolina • Today

Full-time

USD 123,000.00 - 154,000.00 per year

Staff AI Platform Engineer

Sysdig

Remote or San Francisco, California • Today

Full-time

USD 141,000.00 - 194,700.00 per year

Lead Cloud/AI Platform Engineer

VKore Solutions LLC

Remote • Yesterday

Easy Apply

Contract, Third Party

$55 - $60

Principal Data & AI Platform Engineer, DataOps

TelaDoc

Remote • Today

Full-time

USD 200,000.00 - 230,000.00 per year

Sr Staff AI Platform Engineer

WEX Inc

Remote • Today

Full-time

USD 220,000.00 - 255,800.00 per year

AI Infrastructure Platform Engineer (Python)

Bayside Solutions

Remote • Today

Easy Apply

Contract

$55 - $65 per hour

AI Cloud Platform Engineer

Allstate Insurance Company

Remote or Texas • Today

Full-time

USD 85,000.00 - 145,075.00 per year

AI Platform and Harness Engineer

LTS

Remote • 7d ago

Easy Apply

Full-time

133000 - 164000

Principal AI/ML Platform Engineer - Remote

UnitedHealth Group

Remote or Eden Prairie, Minnesota • Today

Full-time

USD 164,600.00 - 282,200.00 per year

Full Stack AI Engineer Python, LangGraph & React

Spiceorb

Toronto, Ontario • 7d ago

Easy Apply

Contract

Depends on Experience

Software Engineer (Cloud / AI Platform)

Bayside Solutions

Remote • Today

Easy Apply

Contract

$50 - $60 per hour

Staff AI Platform Engineer, Infrastructure Services

SentinelOne

Remote • Today

Full-time

USD 156,000.00 - 215,000.00 per year

Backend Software Engineer - Python & Pipeline Orchestration (Cloud / AI Platform)

Bayside Solutions

Remote • Today

Easy Apply

Contract

$60 - $70 per hour

Principal Engineer, AI Platform

Pinterest, Inc.

Remote or San Francisco, California • Today

Full-time

USD 242,634.00 - 499,541.00 per year

Lead AI/ML Systems Engineer

GlobalLogic Inc.

Toronto, Ontario • Yesterday

Easy Apply

Full-time, Third Party

Depends on Experience

FEA Engineer - CalculiX? AI Training (Remote, Freelance)

Braintrust

Remote or Toronto, Ontario • Today

Part-time

Sr. Staff Software Engineer, AI Platform

Pinterest, Inc.

Remote or Palo Alto, California • Today

Full-time

USD 208,592.00 - 429,454.00 per year

Senior AI Platform DevOps Engineer

Feuji Inc

Remote • 2d ago

Easy Apply

Contract

Depends on Experience

Lead Software Engineer, AI Platform

Root Inc.

Remote • Today

Full-time

USD 164,200.00 - 240,000.00 per year

Staff/ Principal Software Engineer (L6/L7): AI Platform Model Serving

Netflix, Inc.

Remote • Today

Full-time

Compensation information provided in the description

Job Details

Skills

Artificial Intelligence
LangChain
Kubernetes

Summary

Position Title: Senior Platform Engineer - Nearshore

Location: Canada (Remote)

Type: Contract

Build, deploy, and operate the shared AI platform services that application teams depend on LLM gateway and proxy layers, model routing and fallback, MCP servers and registries, agent runtimes, and the supporting APIs around them with telemetry, health signals, and cost attribution designed in rather than added later.

Onboard and enable new model providers and model versions across environments, including routing rules, rate limits and quotas, model tiering and cost-aware selection, fallback behavior, and version deprecation paths, and make each provider's usage, latency, error, and spend profile visible and comparable.
Design and operate the MCP layer that exposes enterprise systems as tools to agents server deployment, tool registration and discovery, connection and session handling, safe tool-permission boundaries, and tracing of tool calls, retries, and failures.

Support the agent lifecycle on the platform publishing and versioning, registry and discovery, memory and session management, scaling and resilience and instrument agent steps, state transitions, and evaluation outcomes so behavior can be explained after the fact.

Run platform services on Kubernetes using GitOps practices: Helm and Kustomize manifests, declarative delivery, autoscaling, resource tuning, health checks, and zero-downtime or progressive rollout.
Implement authentication, authorization, and tenancy for the platform OAuth2/OIDC and SSO integration, JWT issuance and validation, role and group based access, virtual key and API key management, and secrets handling through a managed key vault.

Apply guardrails and policy enforcement, including PII detection with masking or blocking, prompt and response filtering, content policy, audit logging, and retention rules for prompts, completions, and traces.
Partner with engineering and developer teams to define observability standards for AI applications, LLM integrations, and agentic workflows, and make those standards the default path rather than an extra step.
Implement and maintain telemetry patterns in Langfuse, including traces, spans, prompts, completions, feedback, evaluation metadata, latency, errors, token usage, and model/provider context.
Instrument the platform itself with OpenTelemetry, metrics, and structured logs so gateway, MCP, and agent-runtime behavior is traceable end to end alongside application-level AI traces.
Build dashboards, reports, and alerts that help teams understand AI reliability, performance, quality, evaluation outcomes, and production behavior.
Design cost and usage observability across LLM vendors such as OpenAI, Anthropic, Google Gemini, and other providers, including attribution by application, team, user, model, workflow, and environment.
Create showback or chargeback-ready metrics for token usage, request volume, model mix, latency, cache behavior, evaluation runs, and vendor spend, and feed what they show back into routing, tiering, and capacity decisions.

Support AI evaluation practices by helping teams define test sets, golden datasets, scoring strategies, prompt and version comparisons, regression checks, and release readiness signals.
Analyze execution patterns across agent tooling such as Claude Code, in-house agent frameworks, LangChain, LangGraph, CrewAI, and Google ADK, and turn what you find into platform fixes and guidance.
Support retrieval and context-grounding infrastructure embedding models, vector and graph stores, ingestion and refresh pipelines, and retrieval quality measurement and tuning.
Build the developer-facing self-service surface: onboarding automation, provisioning workflows, templates, and internal portals or CLIs that let teams get access, register agents and tools, and ship without manual tickets.
Extend observability into the software delivery layer where relevant AI-assisted development telemetry, pipeline and pull-request lifecycle metrics, and DORA-style delivery signal

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10496476a
Position Id: 9102309
Posted 30+ days ago

Fitment

Dice Job Match Score™

To see how well you match this job, please log in or create an account.

Once logged in, be sure to complete your profile to get the most accurate match score.

Contact the job poster

Sunil Joshi

Recruiter @ Diligente Technologies

View Profile

Create job alert

Never miss an opportunity! Create an alert based on the job you applied for.

Similar Jobs

AI Platform Engineer

Remote

•

Today

Platform Engineer Agentic AI & Developer Platforms We are seeking an experienced Platform Engineer to join a high impact engineering team focused on building next generation AI powered developer platforms. This role will play a critical part in designing, developing, and operationalizing internal AI agent platforms, MCP servers, automation frameworks, and developer tooling that enable engineering teams to build and deploy software more effectively. The ideal candidate brings hands on experience

Easy Apply

Contract

Depends on Experience

Senior AI Platform Engineer

Remote

•

Today

About the role We are seeking a Senior AI Platform Engineer to design, build, deploy, and support enterprise AI solutions and the platform capabilities that enable them. This is a hands-on software engineering role focused on enabling the development of AI tools that will transform Blackbaud's business processes. You will build agentic and LLM-powered solutions while establishing the reusable engineering patterns, security controls, observability, and operational practices needed to use AI saf

Full-time

USD 117,200.00 - 157,500.00 per year

Senior Software Engineer, Backend (AI Platform)

Remote

•

Today

At Affirm, we exist for the moments that matter-giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Affirm's engineering team is building a large-scale, fault-tolerant, and global infrastructure that is shared across multiple financial products, merchants, and vendors. Ensuring that our infrastructure is transparently available to all engineers is critical to the success of the business. We pride ourselves on our cul

Full-time

USD 195,000.00 - 255,000.00 per year

Senior Software Engineer - AI Infrastructure

No location provided

•

Today

Overview This role is open to candidates in Boston, New York, Washington, D.C., Chicago, LA, Silicon Valley and San Francisco. The Cornerstone Research Data Science Center is seeking a Senior Software Engineer to design, build, harden, and scale infrastructure that powers AI applications across the firm. This is a hands-on role within a versatile software engineering team that operates with a high degree of autonomy and scope. You will co-own the platform layer beneath the firm's AI tooling,

Full-time

USD 215,000.00 - 235,000.00 per year

Job Details

Skills

Artificial Intelligence
LangChain
Kubernetes

Summary

Position Title: Senior Platform Engineer - Nearshore

Location: Canada (Remote)

Type: Contract

Build, deploy, and operate the shared AI platform services that application teams depend on LLM gateway and proxy layers, model routing and fallback, MCP servers and registries, agent runtimes, and the supporting APIs around them with telemetry, health signals, and cost attribution designed in rather than added later.

Onboard and enable new model providers and model versions across environments, including routing rules, rate limits and quotas, model tiering and cost-aware selection, fallback behavior, and version deprecation paths, and make each provider's usage, latency, error, and spend profile visible and comparable.
Design and operate the MCP layer that exposes enterprise systems as tools to agents server deployment, tool registration and discovery, connection and session handling, safe tool-permission boundaries, and tracing of tool calls, retries, and failures.

Support the agent lifecycle on the platform publishing and versioning, registry and discovery, memory and session management, scaling and resilience and instrument agent steps, state transitions, and evaluation outcomes so behavior can be explained after the fact.

Run platform services on Kubernetes using GitOps practices: Helm and Kustomize manifests, declarative delivery, autoscaling, resource tuning, health checks, and zero-downtime or progressive rollout.
Implement authentication, authorization, and tenancy for the platform OAuth2/OIDC and SSO integration, JWT issuance and validation, role and group based access, virtual key and API key management, and secrets handling through a managed key vault.

Apply guardrails and policy enforcement, including PII detection with masking or blocking, prompt and response filtering, content policy, audit logging, and retention rules for prompts, completions, and traces.
Partner with engineering and developer teams to define observability standards for AI applications, LLM integrations, and agentic workflows, and make those standards the default path rather than an extra step.
Implement and maintain telemetry patterns in Langfuse, including traces, spans, prompts, completions, feedback, evaluation metadata, latency, errors, token usage, and model/provider context.
Instrument the platform itself with OpenTelemetry, metrics, and structured logs so gateway, MCP, and agent-runtime behavior is traceable end to end alongside application-level AI traces.
Build dashboards, reports, and alerts that help teams understand AI reliability, performance, quality, evaluation outcomes, and production behavior.
Design cost and usage observability across LLM vendors such as OpenAI, Anthropic, Google Gemini, and other providers, including attribution by application, team, user, model, workflow, and environment.
Create showback or chargeback-ready metrics for token usage, request volume, model mix, latency, cache behavior, evaluation runs, and vendor spend, and feed what they show back into routing, tiering, and capacity decisions.

Support AI evaluation practices by helping teams define test sets, golden datasets, scoring strategies, prompt and version comparisons, regression checks, and release readiness signals.
Analyze execution patterns across agent tooling such as Claude Code, in-house agent frameworks, LangChain, LangGraph, CrewAI, and Google ADK, and turn what you find into platform fixes and guidance.
Support retrieval and context-grounding infrastructure embedding models, vector and graph stores, ingestion and refresh pipelines, and retrieval quality measurement and tuning.
Build the developer-facing self-service surface: onboarding automation, provisioning workflows, templates, and internal portals or CLIs that let teams get access, register agents and tools, and ship without manual tickets.
Extend observability into the software delivery layer where relevant AI-assisted development telemetry, pipeline and pull-request lifecycle metrics, and DORA-style delivery signal

Dice Id: 10496476a
Position Id: 9102309
Posted 30+ days ago

Fitment