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Scrobits Technologies

HR Generalist - Scrobits

We are looking for an experienced Generative AI Engineer who can blend strong Python backend engineering with modern AI frameworks to build scalable, production-grade AI systems. The ideal candidate has hands-on expertise with LangChain, LangGraph, FastAPI, RAG pipelines, vector databases, and agentic workflows, and can translate complex business problems into reliable AI-driven solutions.ResponsibilitiesGenerative AI &amp; LLM Engineering<ul><li>Build and optimize RAG pipelines using vector DBs like Pinecone, Weaviate, Supabase, PGVector, etc.</li><li>Develop AI workflows using LangChain, LangGraph, and modern model orchestration tools.</li><li>Implement prompt engineering, prompt templates, response validation, and LLM optimisation.</li><li>Work with embeddings, similarity search, and text-to-vector processing for personalized retrieval.</li><li>Build agentic AI workflows capable of multi-step reasoning, tool usage, and autonomous task execution.</li><li>Handle multimodal workloads involving text, images, or video for processing or generation.</li><li>Integrate observability tools for tracking latency, cost, accuracy, and model behavior.</li></ul>Backend Engineering (Python)<ul><li>Build scalable backend services using FastAPI (preferred), Django, or Flask.</li><li>Design RESTful APIs and backend pipelines for AI workloads, streaming, and multimodal data.</li><li>Implement asynchronous programming and background job execution (Celery, cron, or equivalents).</li><li>Work with relational DBs (PostgreSQL, MySQL) and integrate vector DBs for AI workflows.</li><li>Consume 3rd-party SDKs/APIs, ensuring secure and seamless system integrations.</li><li>Deploy services using Docker, containerized pipelines, and production-grade best practices.</li><li>Implement secure coding practices, RBAC, API security, and scalable architectures.</li></ul>Required Skills<ul><li>Strong proficiency in Python and backend frameworks (FastAPI preferred).</li><li>Deep experience with LangChain, LangGraph, RAG pipelines, vector databases, embeddings.</li><li>Hands-on knowledge of LLMs, prompt engineering, multimodal models, and AI agents.</li><li>Experience deploying AI services with Docker, job schedulers, and scalable backend design.</li><li>Strong understanding of relational DBs + vector DBs.</li><li>Experience with asynchronous programming and writing robust unit/integration tests (Pytest).</li><li>Ability to debug, optimize, and productionize AI applications.</li></ul>Preferred Skills<ul><li>Experience with cloud platforms: AWS, GCP, Azure.</li><li>Basic understanding of frontend technologies (HTML/CSS/JS) for collaboration.</li><li>Familiarity with FastMCP or other model-context-protocol integrations.</li><li>Experience with model monitoring, observability, and performance dashboards.</li><li>Awareness of current trends in generative AI, agentic systems, and multimodal architectures.</li></ul>Who Will Succeed in This Role?Someone who:<ul><li>Can think end-to-end — from data ingestion to retrieval to orchestration to deployment.</li><li>Enjoys solving complex AI problems with clean, scalable backend design.</li><li>Is hands-on with modern AI tooling, not just theoretical knowledge.</li><li>Thrives in a fast-paced environment where innovation and ownership matter.</li></ul>&nbsp;

Generative AI Engineer (Python + RAG + Agentic AI)