Ai-Engineer - job description WHAT YOU’LL DO: Build and deploy end-to-end AI agent architectures with reasoning, planning, and self-correction capabilities.
Fine-tune and optimize large language models to enable agentic behavior grounded in real-world data and context. Develop advanced prompts, workflows, and coordination strategies for multi-agent systems and task orchestration. Integrate AI agents with external APIs, tools, and databases; ensure scalable and performant backend infrastructure. Apply MLOps best practices for deployment, monitoring, versioning, and observability of agentic systems. Explore and adopt emerging agentic AI frameworks and techniques to continuously improve system intelligence. Perform exploratory data analysis and build dashboards to track KPIs and system performance. Collaborate with data, engineering, and product teams to translate business needs into impactful AI solutions. Identify and optimize high-impact automation opportunities through process mapping and systems thinking. Build secure, reliable, and scalable MLOps pipelines, balancing strong technical execution with business value and ROI.
WHAT WE EXPECT FROM YOU:
Programming Proficiency:
Proven experience in developing and deploying AI agents or autonomous systems.
Strong command of Python and AI/ML libraries such as PyTorch and TensorFlow.
AI/ML Expertise:
Hands-on work with LLMs and generative AI frameworks.
Deep knowledge of prompt engineering, RAG pipelines, and vector databases.
Data Wrangling & EDA:
Skilled in data acquisition, transformation (ETL), and validation for AI model readiness.
Software Engineering (AI-Infused):
Strong system design, testing, and version control (Git) fundamentals.
Backend development experience for AI/ML pipelines.
Familiarity with Google cloud platform.
Proven track record using AI-assisted workflows for solution development.
Agentic Frameworks:
Practical experience with Claude Code, CrewAI, LangChain, LlamaIndex.
Additional experience:
Experience with conversational AI and chatbot systems.
Awareness of LLM security challenges (e.g., prompt injection).
Exposure to high-scale AI model deployment in low-latency environments.
A public portfolio or GitHub projects demonstrating applied AI agent development.
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YOUR JOURNEY WITH US:
Step 1: Pre-screen.
Step 2: Interview.
Step 3: Test task.
Step 4: Reference check.
Step 5: Job Offer!
28 business days of paid off.
Flexible hours and the possibility to work remotely.
Medical insurance and mental health care.
Compensation for courses, trainings.
English classes and speaking clubs.
Internal library, educational events.
Outstanding corporate parties, teambuildings.
WHAT WE OFFER:
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