Role Overview

We are looking for a Full-Cycle AI Engineer who can take a raw business idea, analyze its viability, and independently build high-impact Proof of Concepts (PoC) and Minimum Viable Products (MVP) using modern AI frameworks and LLMs. You will own the entire lifecycle: from initial process analysis and architecture design to hands-on development and rapid deployment.

Key Responsibilities

Ideation & Process Analysis: Analyze business workflows to identify high-value opportunities for Generative AI and Machine Learning. Translate vague business ideas into concrete technical requirements for AI systems.

Rapid PoC & MVP Development: Fast prototyping and implementation of end-to-end AI applications. Build functional MVPs independently, selecting the right architectural patterns (RAG, Agentic workflows, Fine-tuning).

System Integration & Architecture: Design and implement scalable integrations (APIs, webhooks, microservices) to connect AI solutions with existing internal infrastructure and databases.

Evaluation & Optimization: Set up evaluation frameworks to benchmark AI model performance, accuracy, cost, and latency. Continuously optimize MVP solutions based on user feedback.

Required Skills and Qualifications

AI Architecture & Frameworks: Deep understanding of modern AI paradigms (Retrieval-Augmented Generation (RAG), Multi-Agent systems, Function Calling). Hands-on experience with orchestration frameworks like LangChain, LlamaIndex, or CrewAI.

Software Engineering (Full-Cycle): Strong backend development skills with Python (FastAPI, Flask) and basic frontend prototyping abilities (Streamlit, Gradio, or Next.js) to quickly put a UI on an MVP.

LLM API & Vector DB Expertise: Practical experience working with commercial APIs (OpenAI, Anthropic) and open-source models (Ollama, HuggingFace). Experience with Vector Databases (Pinecone, Chroma, Qdrant, or pgvector).

Product Mindset: Ability to cut through noise, prioritize core features for an MVP, and focus on delivering business value quickly rather than over-engineering.

Communication & Stakeholder Management: Ability to bridge the gap between business needs and technical execution. Comfortable explaining technical trade-offs (e.g., cost vs. accuracy) to non-technical teams.

Tools and Technologies

Core Languages: Python (Advanced), SQL.

AI Frameworks: LangChain, LlamaIndex, CrewAI / Autogen, Hugging Face Transformers.

Models & APIs: OpenAI API, Claude (Anthropic), Ollama / Llama 3, Mistral.

Vector Databases: Pinecone, Qdrant, ChromaDB, or Weaviate.

Backend & Deployment: FastAPI, Docker, basic Cloud services (AWS / GCP / Azure), GitHub.

UI Prototyping: Streamlit, Gradio, or Vercel/Next.js.

Experience

1–3 years of experience in AI/ML development, Software Engineering, or Rapid Prototyping roles.

Proven track record of building and deploying functional AI prototypes or MVPs from scratch.