About the Role
We are looking for a skilled AI Developer to design, develop, integrate, and deploy intelligent, production-ready AI applications.
The ideal candidate must have strong hands-on experience in Python, FastAPI, Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI agents, vector databases, and Machine Learning models and evaluating their performance.
The candidate will build AI-powered applications, conversational assistants, intelligent automation workflows, and Python-based AI backend services. The role also involves integrating AI capabilities with existing application backends through REST APIs and developing reliable, scalable AI solutions for real-world business requirements.
We are looking for someone with practical implementation experience who can turn AI concepts into working applications, evaluate their performance, and deploy them into production.
Key Responsibilities
- Design, develop, and maintain production-ready AI applications using Python.
- Build and optimize RAG pipelines for knowledge retrieval and context-aware responses.
- Integrate LLMs from providers such as OpenAI, Google Gemini, Anthropic Claude, or equivalent platforms.
- Develop AI agents with tool calling, function execution, and multi-step workflow orchestration.
- Build AI chatbots, conversational assistants, and natural-language interfaces.
- Design and implement AI-powered workflow automation and intelligent business processes.
- Develop AI backend services and REST API endpoints using FastAPI.
- Integrate AI services with existing backend applications, REST APIs, databases, and third-party services.
- Build AI agents that retrieve application data and execute authorized operations through existing APIs.
- Develop document ingestion, preprocessing, chunking, embedding, indexing, retrieval, and reranking pipelines.
- Implement vector databases and semantic search for efficient knowledge retrieval.
- Apply Machine Learning techniques to suitable prediction, classification, recommendation, and other data-driven use cases.
- Evaluate AI and ML models using appropriate performance metrics and test datasets.
- Work with pretrained models and evaluate opportunities for fine-tuning when required.
- Implement prompt engineering, structured outputs, context management, and response validation.
- Develop multilingual AI capabilities and conversational workflows.
- Implement AI evaluation, testing, guardrails, logging, monitoring, and hallucination-reduction techniques.
- Optimize AI application quality, response latency, reliability, scalability, and model usage costs.
- Deploy, troubleshoot, and maintain AI applications in production environments.
- Follow secure development practices for API integrations, authentication, authorization, and data protection.
- Collaborate with frontend, backend, mobile, QA, and DevOps engineers to integrate AI capabilities into applications.
Mandatory Technical Skills
1. Python and Backend Development
- Strong hands-on experience in Python programming.
- Experience building production-grade backend services using FastAPI.
- Experience developing and consuming REST APIs.
- Understanding of asynchronous programming, request validation, response schemas, exception handling, and testing.
- Ability to integrate AI services with existing backend systems and third-party platforms.
2. LLMs and Generative AI
- Practical experience developing LLM-powered applications.
- Experience integrating LLM providers such as OpenAI, Gemini, Claude, or equivalent platforms.
- Understanding of prompt engineering, context windows, structured outputs, and function/tool calling.
- Ability to build reliable AI applications beyond basic chatbot demonstrations.
3. RAG Pipelines and Knowledge Retrieval
- Hands-on experience designing and implementing RAG architectures.
- Understanding of embeddings, document chunking, semantic search, metadata filtering, and retrieval optimization.
- Experience with vector search and context retrieval.
- Ability to evaluate and improve retrieval relevance and answer accuracy.
4. AI Agents and Workflow Automation
- Experience building AI agents and agentic workflows.
- Understanding of tool calling, API execution, state management, and workflow orchestration.
- Experience with frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent tools.
- Ability to automate multi-step tasks using AI models, APIs, and external tools.
5. Machine Learning Fundamentals
- Understanding of supervised and unsupervised learning.
- Knowledge of common ML algorithms and their appropriate use cases.
- Understanding of data preprocessing, feature engineering, training and validation datasets, and model evaluation.
- Familiarity with metrics such as precision, recall, F1-score, and accuracy, where appropriate.
- Ability to identify when a problem requires traditional Machine Learning versus an LLM-based solution.
6. Vector Databases and Data Integration
- Experience with at least one vector database, such as Qdrant, Pinecone, Weaviate, Milvus, or pgvector.
- Understanding of similarity search, vector indexing, and metadata filtering.
- Experience integrating AI applications with relational databases, NoSQL databases, and external services.
7. Production Deployment and Engineering
- Experience deploying and maintaining AI applications in production.
- Knowledge of Docker, Git, testing, CI/CD, logging, and monitoring.
- Understanding of application security, scalability, reliability, and performance optimization.
- Ability to troubleshoot production issues and maintain reliable AI services.
Preferred Qualifications
- Hands-on experience fine-tuning pretrained LLMs or other ML models for specific use cases.
- Familiarity with PyTorch, TensorFlow, or scikit-learn, as appropriate to the solution.
- Experience with model evaluation, experiment tracking, and dataset preparation.
- Knowledge of deep learning fundamentals and neural network architectures.
- Experience deploying AI applications on Microsoft Azure, AWS, Google Cloud, or another suitable cloud platform.
- Familiarity with managed AI services, including Azure OpenAI, where applicable.
- Experience with speech recognition, text-to-speech, or voice-based AI applications.
- Knowledge of event-driven architecture and distributed systems.
- Familiarity with Go (Golang) is an added advantage.
What Were Looking For
- Strong practical implementation and problem-solving skills.
- Ability to independently develop, test, integrate, and deploy AI solutions.
- A solid understanding of Machine Learning and hands-on experience with modern Generative AI technologies.
- Ability to connect AI capabilities to real application data, backend APIs, and business workflows.
- Strong debugging skills and a focus on clean, maintainable, secure code.
- Ability to work collaboratively with software engineering teams.
- A willingness to learn, experiment, evaluate results, and improve AI systems continuously.
Interested candidates can send their resumes to careers@obbian.com