Senior AI Engineer

O projekcie:
As an AI & LLM Operations Engineer, you will sit at the intersection of software engineering, data engineering, and applied AI. You will design and operate agentic, multi‑agent systems that turn complex business workflows into reliable, production‑grade AI solutions. Partnering closely with engineering, data, and business stakeholders, you will integrate LLM‑powered capabilities into existing platforms, build conversational analytics over enterprise data, and ensure these systems are observable, secure, and scalable. This role is ideal if you bring a strong production and data engineering background and are eager to grow rapidly in AI/ML while shaping how next‑generation AI is built and operated.

Wymagania:
Required Qualifications: - Strong programming skills in Python and experience building production‑grade systems and services; - Proven experience with CI/CD pipelines, GitHub workflows, version control practices, and modern SDLC and engineering best practices; - Deep understanding of production system operations, including monitoring, logging, debugging, incident response, and a focus on observability and reliability; - Solid foundation in data engineering, including data pipelines, SQL, and experience with both relational and NoSQL databases and processing data at scale; - Understanding of data quality, governance, and metadata management, with familiarity with data cataloging, lineage tracking, or knowledge management systems as a plus; - Experience with at least one major cloud platform (AWS, Azure, or GCP) and infrastructure‑as‑code, plus an understanding of security, governance, and compliance in data and AI systems; - Demonstrated production engineering mindset, with a track record of deploying and maintaining reliable systems and handling incident management or on‑call responsibilities; - Strong data and systems thinking, with the ability to design data flows and architectures that balance complexity with maintainability and support downstream applications; - Excellent written and verbal communication skills, including the ability to create documentation and technical specifications and to work effectively with engineering, product, and business teams; - High learning agility and adaptability, with a growth mindset, comfort with ambiguity, and self‑directed learning in AI/ML (e.g., side projects, courses, community involvement); - Active interest and hands‑on experimentation with LLMs and AI tools, with a basic understanding of prompt engineering, LLM capabilities and limitations, and curiosity about multi‑agent systems, RAG architectures, and emerging AI patterns.  Preferred Qualifications:- 5+ years of software engineering experience, including 3+ years focused on data engineering, data platforms, or analytics infrastructure; - Experience building data products that serve business users or analysts and familiarity with data warehousing concepts and platforms (e.g., Snowflake, BigQuery, Redshift); - Experience with workflow orchestration tools (e.g., Airflow, Prefect, Dagster) and background in search, recommendation systems, or information retrieval; - Exposure to AI/ML through side projects, hackathons, or proof‑of‑concepts, including experimentation with vector databases and semantic search technologies; - Prior experience building or contributing to LLM‑powered applications and familiarity with AI safety, responsible AI practices, and ethical AI considerations; - Knowledge of multi‑agent frameworks (e.g., LangGraph, AutoGen, CrewAI, or similar) and experience with developer experience, platform engineering, or internal tooling; - Background in analytics and measurement frameworks, contributions to open‑source projects or technical communities, and experience with change management or technology advocacy; - Prior work in highly regulated industries such as pharmaceuticals, finance, or healthcare.

Codzienne zadania:
- Design and implement multi‑agent orchestration systems that coordinate AI agents across complex, multi‑step workflows (e.g., intake, planning, implementation, evaluation, supervision);
- Build autonomous SDLC pipelines where AI agents support project intake, requirements refinement, GitHub issue creation, and code implementation with appropriate human oversight;
- Integrate AI systems with existing engineering infrastructure, including GitHub, CI/CD pipelines, testing frameworks, and coding standards, to ensure seamless and secure operations;
- Develop natural‑language query interfaces over complex enterprise data, combining structured and unstructured sources so business users can extract insights through conversation;
- Build explainability, guardrails, and scope‑management layers to improve accuracy, handle edge cases gracefully, and increase trust in AI‑driven workflows;
- Design and operate data and system architectures that enable reliable AI operations, including data integration layers, monitoring, logging, and incident response for AI services;
- Collaborate with cross‑functional teams to understand business and data requirements, translate them into technical solutions, and iteratively deliver AI capabilities that drive measurable productivity gains.