Gen AI Engineer – Infogain Poland
About the role: As an entry-level GenAI Engineer, you will assist in designing and developing custom AI solutions, focusing on Generative AI and Large Language Models (LLMs). This role is ideal for recent graduates or early-career professionals with foundational knowledge in ML and Python, eager to contribute to innovative AI tools and prototypes. You will collaborate with senior engineers to integrate AI into platforms for training, consulting, and enterprise support.
Responsibilities:
Design and develop custom chatbot solutions, including retrieval-augmented generation (RAG), dialogue flow control, and intent handling.
Build and deploy Generative AI-powered tools and proofs-of-concept (POCs) for client demos and tool platforms.
Integrate LLMs (e.g., OpenAI, Azure OpenAI, Vertex AI, Cohere, DeepSeek) into multi-step workflows.
Build APIs and backend logic to support chatbot queries, document parsing, user prompts, and output rendering.
Work with structured/unstructured data to build context-aware AI features (e.g., PDF/chat/document assistants, chunking, embedding search).
Fine‑tune LLM behavior using prompt engineering, chaining logic, or external tools like LangChain or LlamaIndex.
Collaborate with frontend and full‑stack engineers to ensure seamless GenAI integration into products.
Evaluate model performance, latency, and cost for optimal deployment strategies.
Stay current with the latest developments in LLMs, GenAI frameworks, open‑source models, and API ecosystems.
Requirements:
Bachelor's degree in Computer Science, Engineering, or a related field.
0–3 years of experience in AI/ML development, with a focus on Generative AI/LLM‑based applications.
Strong proficiency in Python, including experience with APIs, backend integration, and cloud deployment (Azure/GCP preferred).
Hands‑on experience with OpenAI, Azure OpenAI, Google Vertex AI, DeepSeek, Anthropic, or Cohere APIs.
Familiarity with LangChain, LlamaIndex, RAG architecture, and vector databases (e.g., FAISS, Pinecone, Chroma).
Basic knowledge of prompt design, response parsing, multi‑agent flows, and pipeline‑based GenAI tool design.
Understanding of document parsing, embeddings, text chunking, token optimization, and summarization logic.
Experience with Flask, FastAPI, or Django for rapid prototyping and integration.
Foundational knowledge in statistical analysis and classic AI/ML models (e.g., regression, clustering).
Basic exposure to MLOps/LLMOps for CI/CD pipelines is a plus.
Preferred Skills:
Frameworks like LangChain, Semantic Kernel, or Crew AI for building agents and Agentic AI solutions.
Eagerness to learn and contribute to team projects.
General benefits – depends on the form of employment:
Attractively located office with collaboration spaces
Onsite parking space for employees
Referral program with financial bonus
Life Insurance
Budget for development (including language courses and others), clear career path with the possibility to gain experience in international environment
Access to internal Learning Platform with multiple trainings oriented for professional growth
Access to MyBenefit platform (Multisport included)
Team Building activities
Charity initiatives
Working environment promoting diversity and inclusion
Seniority level
Entry level
Employment type
Full-time
Job function
Information Technology
Industries
IT Services and IT Consulting
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