GenAI Engineering Senior Analyst

Phiture; Lisboa; Presencial; EUR 40 000 - 70 000; Tempo integral

Resumo da oferta

A leading technology firm in Portugal is seeking a GenAI Engineer to develop and deploy intelligent solutions. The role involves collaborating with data scientists and engineers to analyze client requirements and design GenAI applications utilizing AI frameworks and cloud technologies. Candidates should have experience with Python, Generative AI models, and cloud platforms like Azure.

Qualificações

• Minimum 1 year of experience in building GenAI solutions.
• Experience in developing cloud applications on Microsoft Azure preferred.
• Proficient in Prompt Engineering.

Responsabilidades

• Analyze and scope client's requirements.
• Propose and design the solution.
• Estimate the effort to deliver the solution.
• Collaborate with cross-functional teams.
• Build GenAI Platform and Product software components.

Conhecimentos

Proficiency in Python

Experience with AI/ML frameworks

Hands-on experience with Generative AI models

Background in NLP or multimodal AI applications

Experience with API development

Knowledge of Docker

Familiarity with databases

Experience with cloud platforms

Strong understanding of software engineering principles

Ability to work in agile environments

Formação académica

Preferred certification in Azure AI Engineering Associate

Ferramentas

FastAPI

Flask

Django

Docker

Azure

Descrição da oferta de emprego

Overview

At Avanade, we see how the power of Dynamics 365 shapes the future of businesses every day. By combining our Microsoft knowledge with our creative approach to innovation and strong industry knowledge, we create opportunities for Microsoft, our clients, and you. Avanade has countless paths for you to pursue. One of them is sure to lead to your unique version of success.

Join us

GenAI Engineering

Artificial Intelligence Engineering involves the development of real-life predictive and intelligent solutions to be embedded in the business processes of our customers. The main focus of GenAI Engineering is the design, development and deployment of GenAI solutions that meet functional and technical programming standards. GenAI Engineers are expected to analyze and scope client’s requirements, propose and design the solution, estimate the effort to deliver the solution and articulate that design and its value to the customer. GenAI Engineers will understand various programming languages, analytics platforms, GenAI frameworks and APIs which they will use to design complex applications.

Skills And Experiences

• Proficiency in Python, with experience in AI/ML frameworks such as TensorFlow, PyTorch, or Hugging Face.
• Hands-on experience with Generative AI models, including LLMs, GANs, VAEs, or Diffusion Models.
• Background in NLP, OCR or multimodal AI applications.
• Experience with API development using FastAPI, Flask, or Django.
• Knowledge of Docker and containerized deployments.
• Familiarity with databases like PostgreSQL, MongoDB, or CosmosDB.
• Experience with cloud platforms (Azure, AWS, or GCP) for AI model deployment.
• Strong understanding of software engineering principles and best practices.
• Ability to work in agile environments and collaborate with cross-functional teams.
• Agentic Frameworks, including AutoGen, Semantic Kernel, LangGraph or Crew AI.

About You

• Preferred certification in Azure AI Engineering Associate
• Experience in developing cloud applications running on Microsoft Azure (preferred), Google Cloud Platform or Amazon Web Service.
• Minimum 1 year of experience in building GenAI solutions (using the above technologies) or in prompt engineering

What You’ll Do

Analyze and scope client’s requirements, propose and design the solution, estimate the effort to deliver the solution and articulate that design and its value to the customer. GenAI Engineers use various programming languages, analytics platforms, GenAI frameworks and APIs to design complex applications. This job at Avanade is a critical part of the broader technical team including data scientists, data engineers, architects, project managers, technical experts and other consultants. GenAI Engineering focuses on GenAI solutions developed in conjunction with data scientists and data engineers on Microsoft Azure cloud environments.

Key Responsibilities

• Understand the business problem to be solved including the people, processes and systems impacted
• Have a view of the limitations of traditional technologies and areas of application for GenAI technologies
• Convert the high-level business requirements into functional and non-functional requirements to support the design phase
• Identify and select appropriate technologies for solving problems and formulate GenAI solution blueprints for development
• Determine the relevant software tools to fill the functional and non-functional requirements of the solution as defined by the client and technical team
• Estimate the effort required to execute the build of the designed solution
• Prototype applications applying formulated GenAI designs and verify the problem/solution fit working with data scientists in a collaborative mode
• Build GenAI Platform and GenAI Product software components working alongside infrastructure engineers, data scientists and other team members
• Engineer solutions for new system roll-outs, enhancements, and maintenance of existing applications
• Deliver solutions that meet programming standards. Develop system specifications, create test plans and conduct project and issue management for assigned scope of work
• Understand various programming languages, analytics platforms, GenAI frameworks and APIs to design complex applications
• Develop, fine-tune, and deploy Generative AI models for real-world applications.
• Design, build, and optimize APIs to integrate AI models into production environments.
• Containerize applications using Docker for efficient deployment and scalability.
• Work with various databases (SQL/NoSQL) to store and manage model inputs/outputs efficiently.
• Collaborate with cross-functional teams to define AI-driven features and solutions.
• Ensure scalability, reliability, and performance of AI-powered systems.
• Conduct research and experimentation to stay ahead of emerging AI trends.
• Implement LLMOps best practices, including model monitoring and continuous integration.
• Proficient in Prompt Engineering