English: C1 – Advanced / Fluent Professional Proficiency
Advanced/Fluent professional proficiency, suitable for technical discussions, meetings, documentation, and collaboration with international teams. Weak English communication may result in rejection because the role requires frequent cross-functional teamwork.
About The Role
The LLM / GenAI Engineer builds production AI systems that combine foundation models, retrieval, structured data, and reliable software services. The role covers RAG applications, tool-using agents, prompt and model optimization, and evaluation workflows that turn language models into dependable product capabilities
You will work with applied scientists, platform engineers, and product teams to move GenAI systems from prototype to production. The work requires equal attention to answer quality, latency, cost, security, observability, and failure recovery across cloud-based deployments.
Key Responsibilities
Design and deploy RAG pipelines using Python, LangChain, LlamaIndex, or custom orchestration frameworks, integrating document ingestion, chunking, embeddings, retrieval, reranking, and response generation
Build agentic workflows that safely connect LLMs to internal APIs, databases, search systems, and business tools with structured outputs, permissions, and recovery paths
Develop evaluation systems using benchmark datasets, golden responses, LLM-as-judge methods, human review, and automated regression testing to measure groundedness, relevance, latency, and cost
Fine-tune and optimize models using supervised fine-tuning, LoRA or QLoRA, prompt optimization, distillation, and model-routing strategies where appropriate
Implement production services with Python, FastAPI, Docker, and cloud infrastructure; integrate model providers such as OpenAI, Anthropic, Google, or open-source models hosted on managed platforms
Instrument applications with tracing, metrics, and alerts to monitor token usage, model quality, hallucination rates, latency, failures, and data drift
Partner with security and platform teams to establish controls for PII handling, prompt injection, data access, model versioning, and safe deployment rollbacks
What We Are Looking For
3–8 years of software engineering, machine learning engineering, or applied AI experience, including at least 1 year delivering LLM or GenAI systems to production
Strong Python skills and experience building reliable backend services, asynchronous workflows, REST APIs, automated tests, and CI/CD pipelines
Hands-on experience with RAG architectures, embedding models, vector databases such as Pinecone, Weaviate, Milvus, Chroma, or pgvector, and hybrid retrieval techniques
Practical knowledge of LLM evaluation, prompt engineering, function calling, structured generation, fine-tuning, and model tradeoffs involving quality, latency, and cost
Experience with at least one major cloud platform—AWS, GCP, or Azure—and containerized deployment using Docker and Kubernetes or an equivalent platform
Bachelor’s or master’s degree in computer science, machine learning, engineering, mathematics, or a related technical field, or equivalent professional experience
Our Company helps small to medsized companies promptly derive actionable insights out of disintegrated business data with AI software. Since 2015, It helps its clients make smarter decisions by implementing intuitive end-to-end BI solutions and rendering AI support services.