Senior AI Engineer

Intellias; Polska; On-site; PLN 180,000 - 360,000; Full time

Job summary

Intellias is seeking an AI Engineer to move AI capabilities from prototype to production, architecting robust Agentic Systems that plan, reason, and execute workflows autonomously for diverse business needs. The candidate will work hands-on with AI models, collaborate with product managers, business partners, and platform teams to deliver high‑value AI agents across the enterprise.

Requirements include a Bachelor's degree with 5 years of software engineering experience or 9 years with AI/ML

Qualifications

• Bachelor’s degree and 5 years of software engineering experience, or 9 years with AI/ML exposure.
• Strong hands-on Python for AI/LLM application development.
• Experience with Agentic AI design and LLM APIs (OpenAI, Azure OpenAI, Anthropic, Gemini).
• 2+ years building with LLMs, including orchestration and vector databases.
• Excellent communication and ability to document technical designs.

Responsibilities

• Architect complex, multi-agent workflows and deploy agents to handle loops and human-in-the-loop interventions.
• Build tool layers for safe interactions with internal APIs, databases, and third-party SaaS platforms.
• Design persistent memory, conversational history, and context management.

Job description

We are seeking a AI Engineer to transition our AI capabilities from "prototype" to "production." In this role, you will not just experiment with models; you will architect robust Agentic Systems that can plan, reason, and execute complex workflows autonomously for a wide variety of business user needs while experimenting with cutting-edge models and tools to push the boundaries of what’s possible.

The ideal candidate is a forward-thinking engineer who thrives on hands-on experimentation with AI models and frameworks, learns quickly, and is naturally curious. You will collaborate closely with product managers, business partners, and platform teams to deliver high-value AI agents that solve real-world problems across the enterprise.

Project Overview:

Client is a leading multi‑brand technology solutions provider to business, government, education and healthcare customers in the United States, the United Kingdom and Canada. A Fortune 500 company and member of the S&P 500 Index, Client was founded in 1984 and employs approximately 10,000 coworkers. For the trailing twelve months ended September 30, 2020, the company generated Net sales over $18 billion.

Broad array of offerings range from hardware and software to integrated IT solutions such as security, cloud, data center and networking

Requirements:

Core Engineering

Bachelor’s degree and 5 years of software engineering experience, with exposure to AI/ML applications OR 9 years of software engineering experience, with exposure to AI/ML applications.

• Programming: Strong hands-on experience with Python for AI/LLM application development.
• Agentic AI: Hands-on experience designing and building AI agents / Agentic AI solutions.
• LLM APIs: Experience integrating and working with LLM model APIs (e.g., OpenAI, Azure OpenAI, Anthropic, Gemini).

2+ years specifically building with LLMs, with deep familiarity in:

• Orchestration: LangChain, LangGraph, or similar state-based frameworks.
• Vector DBs: Pinecone, Weaviate, or pgvector.
• Prompt Engineering: Advanced techniques (Chain-of-Thought, ReAct, Few-Shot).
• Production Mindset: Experience not just building demos, but operating them. You know how to handle rate limits, context window overflows, and non-deterministic errors.
• Soft Skills: Ability to explain “probabilistic software” to non-technical stakeholders—managing expectations that agents are never 100% accurate, but can be 100% useful.
• Communication: Excellent communication skills, with experience in documenting technical designs, sharing insights, and enabling team knowledge transfer.

Responsibilities:

Agentic Engineering & Orchestration

• Workflow Design: Architect complex, multi-agent workflows using Microsoft AI tech stack. Design, Develop and Deploy agents to handle loops, interruptions, and human-in-the-loop interventions.
• Tool Use & Function Calling: Build reliable “tool layers” that allow LLMs to safely interact with internal APIs, databases, and third-party SaaS platforms (e.g., SalesForce, Workday, ServiceNow etc.)
• State Management: Design persistence layers to manage agent memory, conversational history, and context windows efficiently.

Advanced Data & RAG Strategy

• Retrieval Pipelines: Build production-grade data retrieval and integration systems. Optimize vector indexing, document chunking, and re-ranking algorithms to ensure high-precision context retrieval.
• Data Quality: Collaborate with Data Engineers to curate “Golden Datasets” for agent consumption

LLMOps, Evaluation & Quality

• Automated Evaluation: Build CI/CD pipelines for AI that include “LLM-as-a-Judge” testing. Leverage frameworks to score agent outputs for accuracy, hallucination, and safety before deployment.
• Observability: Instrument applications with tracing tools to visualize agent reasoning chains, monitor latency, and debug failures in production.
• Cost Optimization: Monitor token usage and latency, optimizing prompt density and caching strategies to maintain high performance at sustainable costs.
• Prototyping to Production: Rapidly validate new ideas using state-of-the-art models, then refactor successful prototypes into maintainable, tested production code.
• Standards Adoption: Stay ahead of the curve by evaluating emerging technologies to standardize agent connectivity.