Requirements

Expected technologies

AWS

Microsoft Azure

Google Cloud Platform

Python

Optional technologies

LangChain

LlamaIndex

MLflow

Airflow

Kubeflow

Vertex AI

SageMaker

Pandas

scikit-learn

PyTorch

TensorFlow

Our requirements

• Undergraduate degree or higher in Computer Science, Data Science, Engineering, or related field — or equivalent hands-on experience.
• Proven experience in technical roles — e.g., exposure to AI/ML products, platforms, consulting services, solution engineering, or cloud architecture.
• Foundational understanding of public cloud platforms (AWS, Azure, GCP) with experience deploying cloud-based AI solutions.
• Comfortable working across multiple clients and projects, often in parallel, with strong organizational and time management skills.
• A strong foundational understanding of AI/ML workflows — from data preprocessing and model training to evaluation and deployment.
• Familiarity with LLM-based applications, including embeddings, vector databases, and orchestration frameworks (e.g., LangChain, LlamaIndex).
• Working knowledge of MLOps and orchestration tools like MLflow, Airflow, Kubeflow, Vertex AI, or SageMaker.
• Proficiency in Python and experience with common AI/ML libraries such as pandas, scikit-learn, PyTorch, or TensorFlow.
• Strong communication skills, with the ability to simplify complex concepts for diverse audiences including data scientists, engineers, and non-technical stakeholders.
• Excellent English (at least C1 level).
• Excellent time management and multitasking ability — comfortable working across several customer projects and internal teams.

Optional

• Exposure to or interest in sales processes, technical demos, and customer-facing engagements.

Your responsibilities

• Serve as a technical advisor during pre-sales and early engagement stages, helping clients understand what’s possible with AI/LLMs and how it aligns with their strategic business goals.
• Lead or support technical discovery sessions, clarifying business problems, identifying solution patterns, and assessing feasibility.
• Design and communicate high-level solution architectures, including LLM integrations, AI/ML solution designs, retrieval-augmented generation (RAG), vector databases, orchestration pipelines, and data flows.
• Create and deliver technical PoCs and PoVs, demo narratives, and early prototypes to support proposals and shape delivery.
• Contribute to internal R&D by evaluating emerging AI tools, libraries, and frameworks, and distilling insights for internal use and client recommendations.
• Collaborate with sales, delivery, and engineering teams to ensure a smooth transition from pre-sales, through technical scoping and solution design, to hand-off to delivery teams and full implementation.