The project is for one of the world's famous science and technology companies in pharmaceutical industry, supporting initiatives in AWS, AI and data engineering, with plans to launch over 20 additional initiatives in the future.

We are seeking a highly skilled Cloud Engineer to lead the infrastructure design, deployment, and operations of the AI agent orchestration platform on AWS. This role is responsible for building and managing a Kubernetes-native, enterprise-grade platform that supports scalable AI agent workloads across development, QA, and production environments.

Responsibilities

- Design, provision, and manage AWS infrastructure using Terraform, aligned with the AWS Well-Architected Framework.

Core services include:

Amazon EKS

VPC

IAM

Route 53

Own and operate EKS clusters end-to‑end

Managed node group lifecycle management:

Karpenter-based autoscaling

Cluster add‑on lifecycle upgrades

IRSA (IAM Roles for Service Accounts) configuration

Multi-AZ high availability and resilience

CI/CD & GitOps

Build and maintain automated deployment pipelines using:

GitHub Actions

ArgoCD (GitOps)

Implement release strategies:

Canary releases

Security & Compliance

Integrate AWS-native security and governance controls:

AWS WAF

GuardDuty

Security Hub

KMS (encryption)

Secrets Manager

External Secrets Operator

Enforce policy controls using:

Observability & Monitoring

Implement and manage observability stack:

Amazon Managed Prometheus

Amazon Managed Grafana

CloudWatch Container Insights

AWS X‑Ray (distributed tracing)

AI/ML Integration

Leverage AWS AI/ML services to support agent orchestration:

Cost Optimization (FinOps)

Spot Instances

Savings Plans

Karpenter bin‑packing strategies

Scheduled scale‑to‑zero for non‑production environments

Platform & Engineering Collaboration

Partner with platform and ML teams to:

Integrate MCP servers and execution frameworks

Support extensibility of the agent ecosystem

Skills

Must have

4+ years of hands‑on AWS experience

AWS Certifications:

Required: AWS Solutions Architect (Associate or Professional)

Preferred: DevOps Engineer, Security Specialty Kubernetes & EKS Expertise

Strong hands‑on experience with:

EKS cluster provisioning and operations

Managed node groups and Karpenter

Kubernetes RBAC and network policies Infrastructure as Code (Terraform)

Advanced Terraform capabilities:

Remote state management (S3 + DynamoDB)

Security scanning (Checkov, tfsec) AWS Services Proficiency

Deep knowledge of:

EKS, ECR, ALB, Route 53, ACM

IAM, KMS, Secrets Manager

IAM Identity Center

CloudTrail, AWS Config

GuardDuty, Security Hub, AWS WAF AI/ML Exposure

Practical experience with:

SageMaker (model deployment and endpoints)

Comprehend (NLP and PII detection) DevOps & Identity

Experience with:

GitOps tools (ArgoCD or Flux)

CI/CD pipelines for container workloads

GitHub Actions → AWS

EKS OIDC provider integration Observability & Debugging

Familiarity with:

OpenTelemetry

AWS X‑Ray

Strong understanding of:

Pod Security Standards

Admission webhooks

Service account least‑privilege principles

Nice to have

Experience with AI agent frameworks:

LangChain, Claude Agent SDK, or similar

Knowledge of emerging protocols:

A2A (Agent‑to‑Agent)

Familiarity with:

Amazon Bedrock Agents, Knowledge Bases, Guardrails

Namespace isolation

Programming/debugging skills:

Python, Go, or Node.js

AWS Cost Explorer

Languages

The project is for one of the world's famous science and technology companies in pharmaceutical industry, supporting initiatives in AWS, AI and data engineering, with plans to launch over 20 additional initiatives in the future.

We are seeking a highly skilled Cloud Engineer to lead the infrastructure design, deployment, and operations of the AI agent orchestration platform on AWS. This role is responsible for building and managing a Kubernetes-native, enterprise-grade platform that supports scalable AI agent workloads across development, QA, and production environments.

Responsibilities

- Design, provision, and manage AWS infrastructure using Terraform, aligned with the AWS Well-Architected Framework.

Core services include:

Amazon EKS

VPC

IAM

Route 53

Own and operate EKS clusters end‑to‑end

Managed node group lifecycle management:

Karpenter-based autoscaling

Cluster add‑on lifecycle upgrades

IRSA (IAM Roles for Service Accounts) configuration

Multi-AZ high availability and resilience

CI/CD & GitOps

Build and maintain automated deployment pipelines using:

GitHub Actions

ArgoCD (GitOps)

Implement release strategies:

Canary releases

Security & Compliance

Integrate AWS-native security and governance controls:

AWS WAF

GuardDuty

Security Hub

KMS (encryption)

Secrets Manager

External Secrets Operator

Enforce policy controls using:

Observability & Monitoring

Implement and manage observability stack:

Amazon Managed Prometheus

Amazon Managed Grafana

CloudWatch Container Insights

AWS X‑Ray (distributed tracing)

AI/ML Integration

Leverage AWS AI/ML services to support agent orchestration:

Cost Optimization (FinOps)

Spot Instances

Savings Plans

Karpenter bin‑packing strategies

Scheduled scale‑to‑zero for non‑production environments

Platform & Engineering Collaboration

Partner with platform and ML teams to:

Integrate MCP servers and execution frameworks

Support extensibility of the agent ecosystem

Skills

Must have

4+ years of hands‑on AWS experience

AWS Certifications:

Required: AWS Solutions Architect (Associate or Professional)

Preferred: DevOps Engineer, Security Specialty Kubernetes & EKS Expertise

Strong hands‑on experience with:





EKS cluster provisioning and operations

Managed node groups and Karpenter

Kubernetes RBAC and network policies Infrastructure as Code (Terraform)

Advanced Terraform capabilities:

Remote state management (S3 + DynamoDB)

Security scanning (Checkov, tfsec) AWS Services Proficiency

Deep knowledge of:

EKS, ECR, ALB, Route 53, ACM

IAM, KMS, Secrets Manager

IAM Identity Center

CloudTrail, AWS Config

GuardDuty, Security Hub, AWS WAF AI/ML Exposure

Practical experience with:

SageMaker (model deployment and endpoints)

Comprehend (NLP and PII detection) DevOps & Identity

Experience with:

GitOps tools (ArgoCD or Flux)

CI/CD pipelines for container workloads

GitHub Actions → AWS

EKS OIDC provider integration Observability & Debugging

Familiarity with:

OpenTelemetry

AWS X‑Ray

Strong understanding of:

Pod Security Standards

Admission webhooks

Service account least‑privilege principles

Nice to have

Experience with AI agent frameworks:

LangChain, Claude Agent SDK, or similar

Knowledge of emerging protocols:

A2A (Agent‑to‑Agent)

Familiarity with:

Amazon Bedrock Agents, Knowledge Bases, Guardrails

Namespace isolation

Programming/debugging skills:

Python, Go, or Node.js

AWS Cost Explorer

Languages

Hoy

- GKE
- AlloyDB

FirstIgnite makes software for university tech transfer offices. Those are the people who take research

coming out of a university lab and get it patented, licensed, or spun out into a company.

The role

We're hiring a Senior AI Agent Engineer. You'll build the agents in our product, and you'll build the

evals that tell us whether each change made them better or worse.

The work is document-heavy rather than chat. The agents run multi-step, call tools, read long and

inconsistently formatted source material, check it against existing records, and produce output that a

person reviews before anything happens with it.

Accuracy matters more here than speed or novelty. Most of the engineering effort goes into precision,

traceability, and getting the agent to hand off to a human at the right moment.

You'll report to the Head of Engineering and work with product and the full-stack team. If you've

shipped agents before, you've probably had the experience of changing a prompt and having no idea

whether you improved anything. That problem is most of this job.

What you'll do

Design and ship long-running, multi-step, tool-using agents on various AI SDKs and tooling,

included but not limited to the OpenAI Agents SDK, the Anthropic Agent SDK, the Vercel AI SDK,

LangGraph, MCP, and Temporal Cloud.

Wrap our APIs and our partners' APIs as tools an agent can call over MCP. Some of those systems

are old, single-tenant, and outside our control, so a fair amount of the work is translation.

Get structured data out of long documents and match it against records that already exist. Expect

entity resolution and fuzzy matching, and expect much of it to run in batch.

Stand up eval suites using various evaluation frameworks and tooling, included but not limited to

Promptfoo, Braintrust, LangSmith, DeepEval, LLM-as-judge methods, and custom harnesses.

Measure tool-use correctness, trajectory quality, and whether the agent finished the task.

Every agent here produces a draft that a person signs off on. Build the citations and confidence

signals that make that review fast, and give the agent a clear way to elevate.

Sit with product and domain experts and turn vague quality goals into something measurable.

Sometimes the only dataset available for that is tiny, or confidential, or both.

Instrument production traffic, turn real customer interactions into golden datasets, and run them as

regression tests.

Compare models against each other (OpenAI, Anthropic, open-weight), along with prompt

strategies and agent designs, and know what each option costs in latency and quality.

Bootstrap quality signal for features that have no production traffic yet. That usually means

generating synthetic documents and test cases, including the ugly edge cases real customers will

eventually send us, and knowing where synthetic data stops being a good proxy.

Write the templates, docs, and tooling the rest of the team needs to run evals without coming to

you.

Required Qualifications:

3+ years of engineering experience, including hands-on work on LLM or agent systems that real

users touched.

You've evaluated agents, not only models, and you know why single-turn accuracy says little about

a multi-step run.

You've integrated against APIs you don't own, including old ones with bad documentation, and

turned them into something an agent can call reliably.

You're comfortable with document pipelines: pulling data out, normalizing it, and checking it against

a structured source of truth.

You've used at least one LLM evaluation framework, in-house tooling included.

You know how LLM-as-judge methods break down (position bias, verbosity bias, judge drift) and

what to do about it.

You can tell a real regression from noise, and design an experiment that answers the question

being asked instead of a nearby one.

You can read a customer call transcript, work out which failures matter, and ship a fix and an eval

for them.

You write clearly. Engineers won't act on eval results they don't read or don't trust.

You're based somewhere between New York time (ET) and Western European time.



Italy is the

furthest east we can go.

You might currently be titled

Titles are all over the place in this space. If the work above matches what you already do, apply. We'll

go on what you've shipped.

Preferred Qualifications:

You've evaluated retrieval systems: RAG, hybrid search, reranking.

You've worked with agent orchestration frameworks like Temporal, LangGraph, or the OpenAI

Agents SDK, and you know how long-running tool use goes wrong.

You have a background in information retrieval or search relevance.

You've worked somewhere an agent's output carried financial or compliance consequences.

You've built internal tooling that non-engineers used on their own to label and review model output.

This is a fully remote, full-time permanent position available to candidates located within the New York (ET) through Western Europe time zones, with flexible working hours to support collaboration across regions.

The role

We're hiring a Senior AI Agent Engineer. You'll build the agents in our product, and you'll build the

evals that tell us whether each change made them better or worse.

The work is document-heavy rather than chat. The agents run multi-step, call tools, read long and

inconsistently formatted source material, check it against existing records, and produce output that a

person reviews before anything happens with it.

Accuracy matters more here than speed or novelty. Most of the engineering effort goes into precision,

traceability, and getting the agent to hand off to a human at the right moment.

You'll report to the Head of Engineering and work with product and the full-stack team. If you've

shipped agents before, you've probably had the experience of changing a prompt and having no idea

whether you improved anything. That problem is most of this job.

What you'll do

Design and ship long-running, multi-step, tool-using agents on various AI SDKs and tooling,

included but not limited to the OpenAI Agents SDK, the Anthropic Agent SDK, the Vercel AI SDK,

LangGraph, MCP, and Temporal Cloud.

Wrap our APIs and our partners' APIs as tools an agent can call over MCP. Some of those systems

are old, single-tenant, and outside our control, so a fair amount of the work is translation.

Get structured data out of long documents and match it against records that already exist. Expect

entity resolution and fuzzy matching, and expect much of it to run in batch.

Stand up eval suites using various evaluation frameworks and tooling, included but not limited to

Promptfoo, Braintrust, LangSmith, DeepEval, LLM-as-judge methods, and custom harnesses.

Measure tool-use correctness, trajectory quality, and whether the agent finished the task.

Every agent here produces a draft that a person signs off on. Build the citations and confidence

signals that make that review fast, and give the agent a clear way to elevate.

Sit with product and domain experts and turn vague quality goals into something measurable.

Sometimes the only dataset available for that is tiny, or confidential, or both.

Instrument production traffic, turn real customer interactions into golden datasets, and run them as

regression tests.

Compare models against each other (OpenAI, Anthropic, open-weight), along with prompt

strategies and agent designs, and know what each option costs in latency and quality.

Bootstrap quality signal for features that have no production traffic yet. That usually means

generating synthetic documents and test cases, including the ugly edge cases real customers will

eventually send us, and knowing where synthetic data stops being a good proxy.

Write the templates, docs, and tooling the rest of the team needs to run evals without coming to

you.

Required Qualifications:

3+ years of engineering experience, including hands-on work on LLM or agent systems that real

users touched.

You've evaluated agents, not only models, and you know why single-turn accuracy says little about

a multi-step run.

You've integrated against APIs you don't own, including old ones with bad documentation, and

turned them into something an agent can call reliably.

You're comfortable with document pipelines: pulling data out, normalizing it, and checking it against

a structured source of truth.

You've used at least one LLM evaluation framework, in-house tooling included.

You know how LLM-as-judge methods break down (position bias, verbosity bias, judge drift) and

what to do about it.

You can tell a real regression from noise, and design an experiment that answers the question

being asked instead of a nearby one.

You can read a customer call transcript, work out which failures matter, and ship a fix and an eval

for them.

You write clearly. Engineers won't act on eval results they don't read or don't trust.

You're based somewhere between New York time (ET) and Western European time. Italy is the

furthest east we can go.

You might currently be titled

Titles are all over the place in this space. If the work above matches what you already do, apply. We'll

go on what you've shipped.

Preferred Qualifications:

You've evaluated retrieval systems: RAG, hybrid search, reranking.

You've worked with agent orchestration frameworks like Temporal, LangGraph, or the OpenAI

Agents SDK, and you know how long-running tool use goes wrong.

You have a background in information retrieval or search relevance.

You've worked somewhere an agent's output carried financial or compliance consequences.

You've built internal tooling that non-engineers used on their own to label and review model output.

This is a fully remote, full-time permanent position available to candidates located within the New York (ET) through Western Europe time zones, with versátil working hours to support collaboration across regions.

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📌 Cloud Platform Engineer (Agentic Ai) (Santander)
🏢 Luxoft Spain
📍 Santander