Intermediate Software Engineer - Artificial Intelligence (AI)

📋 Description

• Design AI features for the domain services platform using Python and Go.
• Integrate and fine-tune open-source models (e.g., LLaMA 3.2) via Ollama.
• Research and implement emerging AI tech for smarter products.
• Collaborate with teams to rapidly prototype ML/LLM features.
• Contribute to scalable, high-performance AI stack with ethical usage.
• Engage in open-source AI ecosystem and share tools with the team.

🎯 Requirements

• Bachelor's degree in Software Engineering, Computer Science, or related field.
• 3+ years of professional software engineering experience in production environments.
• Strong proficiency in Python and Golang.
• Solid foundation in software design principles, patterns, and SOA.
• Experience contributing to scalable systems and component-level architecture.
• Ability to design and build RESTful APIs for model serving and AI-enabled workflows.
• Working knowledge of relational/SQL databases (preferably PostgreSQL) and data modeling for AI use cases.
• Strong understanding of modern LLM concepts and transformer architectures.
• Hands-on experience adapting and deploying open-source models (LLaMA, Mistral, Mixtral) using Ollama or Hugging Face Transformers.
• Experience with fine-tuning techniques (LoRA, QLoRA, PEFT) for domain adaptation.
• Proficiency in prompt engineering (few-shot, chain-of-thought, structured outputs).
• Familiarity with model serving patterns for scalable inference.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines end-to-end.
• Hands-on experience with vector databases (pgvector, Pinecone, Weaviate).
• Familiarity with embedding models, chunking strategies, and semantic search patterns.
• Understanding of data pipelines for ingestion, transformation, and inference result storage.
• Familiarity with Model Context Protocol (MCP) server design patterns.
• Experience with agent orchestration frameworks (LangChain, LangGraph).
• Understanding of tool use, function calling, and multi-step reasoning in LLM workflows.
• Experience with LLM evaluation frameworks (RAGAS, promptfoo, or custom pipelines).
• Familiarity with observability and tracing tools (LangSmith, Helicone).
• Comfort with structured logging, metrics, and alerting for AI workloads.
• Experience with containerization and cloud-native deployment (AWS).
• Familiarity with Kubernetes or EKS for scaling model-serving workloads.
• Understanding of GPU considerations for inference (quantization, batching, memory trade-offs).
• Active interest in the open-source AI ecosystem.
• Strong collaboration and communication skills across technical and business teams.
• Enthusiasm for emerging AI technologies with a delivery-focused mindset.

🎁 Benefits

• Remote-first culture; work from anywhere with Internet.
• Global team across 20+ countries.
• Commitment to diversity and inclusion.
• Reasonable accommodations for applicants with disabilities.