Job
Description
Key Responsibilities:
Generative AI Development
? Develop and implement generative AI models using frameworks like LangChainor Llama-Index.? Apply prompt engineering techniques to design effective queries and ensureoptimal LLM responses for diverse use cases.? Master advanced LLM functionalities, including prompt optimization,hyperparameter tuning, and response caching.? Implement Retrieval-Augmented Generation (RAG) workflows by integratingvector databases like Pinecone, Weaviate, Supabase, or PGVector for efficientsimilarity searches.? Work with embeddings and build solutions that leverage similarity search forpersonalized query resolution.? Explore and process multimodal data, including image and video understandingand generation.? Integrate observability tools for monitoring and evaluating LLM performance toensure system reliability.Backend Engineering
? Build and maintain scalable backend systems using Python frameworks such asFastAPI, Django, or Flask.? Design and implement RESTful APIs for seamless communication betweensystems and services.? Optimize database performance with relational databases (PostgreSQL, MySQL)and integrate vector databases (Pinecone, PGVector, Weaviate, Supabase) foradvanced AI workflows.? Implement asynchronous programming and adhere to clean code principles formaintainable, high-quality code.? Seamlessly integrate third-party SDKs and APIs, ensuring robust interoperabilitywith external systems.? Develop backend pipelines for handling multimodal data processing, andsupporting text, image, and video workflows.? Manage and schedule background tasks with tools like Celery, cron jobs, orequivalent job queuing systems.? Leverage containerization tools such as Docker for efficient and reproducibledeployments.? Ensure security and scalability of backend systems with adherence to industrybest practices.Qualifications:
Essential:
? Strong Programming Skills: Proficiency in Python and experience with backendframeworks like FastAPI, Django, or Flask.? Generative AI Expertise: Knowledge of frameworks like LangChain, Llama-Index,or similar tools, with experience in prompt engineering and Retrieval-AugmentedGeneration (RAG).? Data Management: Hands-on experience with relational databases (PostgreSQL,MySQL) and vector databases (Pinecone, Weaviate, Supabase, PGVector) forembeddings and similarity search.? Machine Learning Knowledge: Familiarity with LLMs, embeddings, andmultimodal AI applications involving text, images, or video.? Deployment Experience: Proficiency in deploying AI models in productionenvironments using Docker and managing pipelines for scalability and reliability.? Testing and Debugging: Strong skills in writing and managing unit andintegration tests (e.g., Pytest), along with application debugging andperformance optimization.? Asynchronous Programming: Understanding of asynchronous programmingconcepts for handling concurrent tasks efficiently.Preferred:? Cloud Proficiency: Familiarity with platforms like AWS, GCP, or Azure, includingserverless applications and VM setups.? Frontend Basics: Understanding of HTML, CSS, and optionally JavaScriptframeworks like Angular or React for better collaboration with frontend teams.? Observability and Monitoring: Experience with observability tools to track andevaluate LLM performance in real-time.? Cutting-Edge Tech: Awareness of trends in generative AI, including multimodalAI applications and advanced agentic workflows.? Security Practices: Knowledge of secure coding practices and backend systemhardening.? Certifications:
Relevant certifications in AI, machine learning, or cloudtechnologies are a plus.Requirements
