Note: The job is a remote job and is open to candidates in USA. Hire Hangar Global is a fast-growing company connecting top talent with global employers. They are seeking a hands-on AI Engineer to design, build, and deploy intelligent systems, working across the full AI development lifecycle from prototyping to production deployment.
Responsibilities
- Design and build AI-powered features, pipelines, and automation workflows from scratch
- Integrate and fine-tune LLMs, embedding models, and other ML systems into production applications
- Develop and maintain RAG pipelines, vector search systems, and agent-based architectures
- Write clean, well-structured code across backend and API layers to support AI feature delivery
- Evaluate, benchmark, and iterate on model outputs to ensure quality and reliability
- Collaborate with cross-functional teams to scope requirements and architect AI solutions
- Stay current with the rapidly evolving AI landscape and proactively introduce relevant tooling and approaches
- Document technical designs, system behaviour, and deployment processes clearly and thoroughly
Skills
- Strong programming skills in Python; solid understanding of software engineering fundamentals
- Hands-on experience building with LLMs (OpenAI, Anthropic, Mistral, or similar) via API and SDK
- Practical experience with RAG architectures, vector databases (Pinecone, Weaviate, Chroma, etc.), and prompt engineering
- Familiarity with AI agent frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI
- Solid understanding of REST APIs and experience integrating third-party services and data sources
- Ability to work autonomously in a fast-paced remote environment with minimal hand-holding
- Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered
- Experience with model fine-tuning, RLHF, or custom training workflows
- Familiarity with MLOps tooling and model deployment pipelines (Docker, cloud functions, etc.)
- Exposure to multimodal systems (vision, audio, or document understanding)
- Background in data engineering or working with structured/unstructured data at scale
- Contributions to open-source AI projects or a strong public portfolio of AI work
Company Overview