Akvelon, Inc

This engagement is focused on building an internal AI platform that enables developers to ship AI-powered services efficiently. Scope includes model connectivity, prompt testing and evaluation, monitoring/observability, and the underlying AI infrastructure layer.

The objective is to improve DevEx and reduce time-to-market for AI features.

Location: Serbia (relocation support available), Croatia, Poland, Portugal

Tasks:

• Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster.
• Implement and maintain Kubernetes-based runtime environments (incl. AKS) for AI workloads.
• Manage infrastructure as code with Terraform (modules, environments, CI/CD automation).
• Support LLM workflows: RAG, agents, prompt experimentation, evaluations, and deployment patterns.
• Integrate and operate tooling such as Azure AI Foundry, LiteLLM, Langfuse, MLflow.
• Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate).
• Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals).
• Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation).

Requirements:

• Strong hands-on experience with Kubernetes in production (preferably AKS).
• Solid Terraform expertise (IaC best practices, multi-env setups).
• Practical experience supporting ML/LLM workloads in a platform or DevOps/MLOps context.
• Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling.
• Understanding of CI/CD, release processes, and production-grade operations.
• Ability to work under tight timelines and deliver pragmatically.

Nice to Have

• Experience building internal developer platforms or “paved roads” for engineering teams.
• Familiarity with LLM evaluation frameworks, prompt testing workflows, and LLM observability.
• Exposure to RAG architectures, vector databases, and agentic patterns.
• Experience with Kubeflow, Argo, and ML lifecycle tooling.

Engagement Type

• Long-term B2B contract.

Team

• You will join a team of 5, with 3 AI Platform Engineers being added.

Location / Timezone

• Remote work from Croatia, Poland, Portugal, and Serbia.
• European working hours.
• Occasionally available for meetings up to 10:00 AM PST (US overlap).

This engagement is focused on building an internal AI platform that enables developers to ship AI-powered services efficiently. Scope includes model connectivity, prompt testing and evaluation, monitoring/observability, and the underlying AI infrastructure layer.

The objective is to improve DevEx and reduce time-to-market for AI features.

Location: Serbia (relocation support available), Croatia, Poland, Portugal

Tasks:

• Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster.
• Implement and maintain Kubernetes-based runtime environments (incl. AKS) for AI workloads.
• Manage infrastructure as code with Terraform (modules, environments, CI/CD automation).
• Support LLM workflows: RAG, agents, prompt experimentation, evaluations, and deployment patterns.
• Integrate and operate tooling such as Azure AI Foundry, LiteLLM, Langfuse, MLflow.
• Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate).
• Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals).
• Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation).

Requirements:

• Strong hands-on experience with Kubernetes in production (preferably AKS).
• Solid Terraform expertise (IaC best practices, multi-env setups).
• Practical experience supporting ML/LLM workloads in a platform or DevOps/MLOps context.
• Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling.
• Understanding of CI/CD, release processes, and production-grade operations.
• Ability to work under tight timelines and deliver pragmatically.

Nice to Have

• Experience building internal developer platforms or “paved roads” for engineering teams.
• Familiarity with LLM evaluation frameworks, prompt testing workflows, and LLM observability.
• Exposure to RAG architectures, vector databases, and agentic patterns.
• Experience with Kubeflow, Argo, and ML lifecycle tooling.

Engagement Type

• Long-term B2B contract.

Team

• You will join a team of 5, with 3 AI Platform Engineers being added.

Location / Timezone

• Remote work from Croatia, Poland, Portugal, and Serbia.
• European working hours.
• Occasionally available for meetings up to 10:00 AM PST (US overlap).

We are looking for an AI Engineer to join a product engineering team focused on introducing LLM-based capabilities into enterprise software and building systems such as MCP servers and agents. The role involves working on AI-driven product features, backend services, and integrations that connect models with tools and data in a scalable production environment.

Location: Serbia (relocation support available), Croatia, Poland, Portugal

Tasks:

• Design and develop AI-powered product features with a focus on LLM-based capabilities;
• Build and maintain MCP servers and agent-based components;
• Develop enterprise-grade web services and backend integrations;
• Work with tools, data sources, and model interfaces across cloud environments;
• Collaborate with engineering teams to deliver production-ready AI solutions.

Requirements:

CORE

• AI Engineer profile with strong SDE background;
• Proficiency in C# or Python;
• Knowledge of Model Context Protocol (MCP) for connecting tools and data into models;
• Experience in developing enterprise-grade web services;
• Experience working with cloud platforms such as Azure, AWS, or GCP.

Nice to have

• Hands-on experience with LLM and RAG engineering, including retrieval design, chunking, vector stores, and structured outputs, using tools such as OpenAI, LangChain, or LlamaIndex;
• Experience in building and orchestrating agentic systems that call tools and reason over retrieved context, using frameworks such as LangGraph or AutoGen, and OpenAI tool calling or the Responses API;
• Familiarity with LLM evaluation and observability tools such as LangSmith or Langfuse;
• Experience with guardrails and security measures such as prompt injection defense and output validation.

We are looking for a Senior or Lead Software Development Engineer to join a cloud data platform initiative focused on managed Kafka connectors. The role centers on accelerating onboarding of first-party connectors, improving reliability and launch readiness, and helping expand a managed connector ecosystem used in data-intensive cloud services.

Tasks

• Accelerate onboarding of new first-party managed Kafka connectors;
• Improve connector quality, reliability, and operational readiness;
• Integrate authentication and platform-specific cloud requirements;
• Translate connector behavior into implementation details, UI specs, and documentation inputs;
• Support launch gates such as probers, load testing, playbooks, and launch reviews.

Responsibilities

• Strong Java experience, as the core workers and connectors are implemented in Java;
• Practical Go experience for API-facing changes and service integration work;
• Practical experience with GCP cloud;
• Experience integrating, debugging, and improving connector-based or data integration systems;
• Ability to work across performance, stability, authentication, and network-compatibility concerns;

• Strong engineering ownership across delivery, launch readiness, and operational quality.

   

Nice to Have:

• Experience with Kafka Connect ecosystems;
• Experience with CDC-oriented connectors or sink/source integrations;

• Exposure to cloud data systems such as BigQuery, Spanner, Bigtable, Iceberg, or Debezium-based workflows.

   

Engagement Type

• B2B contract.

   

Location / Timezone

• Priority location is Krakow, Poland;
• On-site presence at the Akvelon office

We are looking for a Senior or Lead Software Development Engineer to join a cloud data platform initiative focused on managed Kafka connectors. The role centers on accelerating onboarding of first-party connectors, improving reliability and launch readiness, and helping expand a managed connector ecosystem used in data-intensive cloud services.

Tasks

• Accelerate onboarding of new first-party managed Kafka connectors;
• Improve connector quality, reliability, and operational readiness;
• Integrate authentication and platform-specific cloud requirements;
• Translate connector behavior into implementation details, UI specs, and documentation inputs;
• Support launch gates such as probers, load testing, playbooks, and launch reviews.

Responsibilities

• Strong Java experience, as the core workers and connectors are implemented in Java;
• Practical Go experience for API-facing changes and service integration work;
• Practical experience with GCP cloud;
• Experience integrating, debugging, and improving connector-based or data integration systems;
• Ability to work across performance, stability, authentication, and network-compatibility concerns;

• Strong engineering ownership across delivery, launch readiness, and operational quality.

   

Nice to Have:

• Experience with Kafka Connect ecosystems;
• Experience with CDC-oriented connectors or sink/source integrations;

• Exposure to cloud data systems such as BigQuery, Spanner, Bigtable, Iceberg, or Debezium-based workflows.

   

Engagement Type

• B2B contract or Business Incubator 

   

Location / Timezone

• Priority location is Krakow, Poland;
• On-site presence at the Akvelon office