Principal Engineer – GenAI

Jobtailor

Kraków

On-site

PLN 320,000 - 520,000

Full time

6 days ago

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Job summary

Jobtailor is seeking an experienced AI architect in Kraków to define strategy, build prototypes, and guide enterprise generative AI initiatives. You will lead architecture, MLOps/LLMOps, and responsible AI integration while mentoring engineers and aligning technical work with business goals.

The role emphasizes collaboration with product leaders, strong communication, and the ability to navigate rapidly evolving AI technologies in a large enterprise environment.

Qualifications

10+ years of software engineering experience, including significant technical leadership across complex enterprise or distributed systems.
Demonstrated recent experience designing, delivering, and operating AI, machine learning, or generative AI solutions in production
Advanced Python skills
Strong command of software architecture, APIs, testing, and maintainable engineering practices
Hands-on understanding of retrieval, agentic workflows, model selection, prompt design, and evaluation
Experience with cloud platforms, distributed systems, containers, CI/CD, and production observability
Ability to incorporate security, privacy, responsible AI, and operational risk requirements into technical designs
Proven ability to influence architecture and engineering decisions across multiple teams without formal authority
Clear communication skills and ability to explain complex technical tradeoffs to technical and business audiences
Bachelor's or master's degree in computer science, software engineering, or a related field, or equivalent practical experience (preferred)
Experience with generative AI frameworks, vector search, model-serving platforms, and enterprise data integration (preferred)
Experience modernizing large-scale enterprise platforms or building reusable internal technology capabilities (preferred)
Experience evaluating AI vendors, commercial models, open models, and total cost of ownership (preferred)
Record of mentoring senior engineers or building technical communities of practice (preferred)
Pragmatic curiosity and bias toward evidence, learning, and measurable outcomes
Comfort navigating ambiguity, changing priorities, and rapidly evolving technology
Collaborative, inclusive leadership style
Strong judgment about experimentation, standardization, and stopping

Responsibilities

Set and evolve technical strategy, reference architectures, and engineering standards for enterprise generative AI solutions
Architect and build prototypes and production services using Python, APIs, cloud platforms, and modern software engineering practices
Lead evaluation of foundation models, retrieval-augmented generation, agentic workflows, and model or vendor options
Establish LLMOps and MLOps practices for evaluation, testing, versioning, deployment, observability, incident response, and continuous improvement
Embed responsible AI, data protection, security, and human-oversight requirements into solution design and delivery
Partner with business and product leaders to frame use cases, define measurable outcomes, and prioritize experiments
Integrate generative AI capabilities with existing applications, enterprise data, and business processes
Lead architecture and design reviews and mentor engineers across teams
Monitor advances in AI and machine learning and recommend adoption when supported by business value
Promote disciplined experimentation with explicit learning goals, evaluation criteria, and decision gates
Guide design decisions and communicate tradeoffs to technical and business leaders

Skills

Advanced Python

Software Architecture

APIs

Machine Learning

Generative AI Frameworks

Model Evaluation

Prompt Design

Testing Practices

Distributed Systems

CI/CD

Production Observability

Vector Search

Education

Bachelor's Degree in Computer Science

Master's Degree in Software Engineering

Tools

Cloud Platforms

Containers

Model-Serving Platforms

Vector Search

Job description

Set and evolve technical strategy, reference architectures, and engineering standards for enterprise generative AI solutions
Architect and build prototypes and production services using Python, APIs, cloud platforms, and modern software engineering practices
Lead evaluation of foundation models, retrieval-augmented generation, agentic workflows, and model or vendor options
Establish LLMOps and MLOps practices for evaluation, testing, versioning, deployment, observability, incident response, and continuous improvement
Embed responsible AI, data protection, security, and human-oversight requirements into solution design and delivery
Partner with business and product leaders to frame use cases, define measurable outcomes, and prioritize experiments
Integrate generative AI capabilities with existing applications, enterprise data, and business processes
Lead architecture and design reviews and mentor engineers across teams
Monitor advances in AI and machine learning and recommend adoption when supported by business value
Promote disciplined experimentation with explicit learning goals, evaluation criteria, and decision gates
Guide design decisions and communicate tradeoffs to technical and business leaders

Requirements

10+ years of software engineering experience, including significant technical leadership across complex enterprise or distributed systems
Demonstrated recent experience designing, delivering, and operating AI, machine learning, or generative AI solutions in production
Advanced Python skills
Strong command of software architecture, APIs, testing, and maintainable engineering practices
Hands-on understanding of retrieval, agentic workflows, model selection, prompt design, and evaluation
Experience with cloud platforms, distributed systems, containers, CI/CD, and production observability
Ability to incorporate security, privacy, responsible AI, and operational risk requirements into technical designs
Proven ability to influence architecture and engineering decisions across multiple teams without formal authority
Clear communication skills and ability to explain complex technical tradeoffs to technical and business audiences
Bachelor's or master's degree in computer science, software engineering, or a related field, or equivalent practical experience (preferred)
Experience with generative AI frameworks, vector search, model-serving platforms, and enterprise data integration (preferred)
Experience modernizing large-scale enterprise platforms or building reusable internal technology capabilities (preferred)
Experience evaluating AI vendors, commercial models, open models, and total cost of ownership (preferred)
Record of mentoring senior engineers or building technical communities of practice (preferred)
Pragmatic curiosity and bias toward evidence, learning, and measurable outcomes
Comfort navigating ambiguity, changing priorities, and rapidly evolving technology
Collaborative, inclusive leadership style
Strong judgment about experimentation, standardization, and stopping

Core Competencies

Demonstrates expertise in architecting and delivering generative AI solutions, with a strong focus on Python, APIs, and cloud platforms. Proven ability to lead technical strategy, mentor engineers, and integrate responsible AI practices into solution design.

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