Job Opportunities API

Our customer's product is an AI-powered platform that helps businesses make better decisions and work more efficiently. It uses advanced analytics and machine learning to analyze large amounts of data and provide useful insights and predictions. The platform is widely used in various industries, including healthcare, to optimize processes, improve customer experiences, and support innovation. It integrates easily with existing systems, making it easier for teams to make quick, data-driven decisions to deliver cutting-edge solutions. Requirements Bachelor’s or Master’s degree in Computer Science or a related field. Strong Python coding skills - 7+ years. 2+ years of hands-on experience with machine learning and production LLM systems. Experience building backend APIs with FastAPI, async patterns, rate limiting, and SQLAlchemy - 3+ years. Experience designing maintainable and extensible systems using dependency injection, interfaces, and abstract base classes. Experience with vector databases such as Pinecone, Weaviate, or Chroma, as well as hybrid search. Strong understanding of RAG architectures, including retrieval, reranking, context assembly, and response generation. Hands-on experience with LangChain and LangGraph for building and orchestrating LLM workflows. Advanced Python skills, including async/await, type hints, Pydantic, and SOLID principles. MLOps experience with MLflow, model versioning, and A/B testing; experience with Langfuse is a plus. Experience in NLP and computer vision, including document understanding, OCR, and GPT-4 Vision. Experience building feature pipelines, real-time and batch inference systems, and model serving. Hands-on experience with Hugging Face is required; experience with LlamaIndex is a plus. Familiarity with database technologies such as SQL. Good problem-solving skills and the ability to work in a fast-paced, team-oriented environment. Nice to have skills: Understanding of DevOps, CI / CD including: Docker containerization, Azure DevOps pipelines or GitHub Actions, Kubernetes (nice to have); Data security including: Multi-tenant data isolation, Secure key management (Azure Key Vault), Audit trail implementation; Experience in designing on cloud platform including: Azure (strongly preferred): Azure OpenAI, Blob Storage, Key Vault, Container Registry, AWS or GCP; Experience in data engineering in Big Data systems including: Large-scale data processing, ETL/ELT pipelines. Rate limiting and quota management for high-throughput API usage. Cost management and optimization for LLM usage at scale. Document processing expertise (PDF extraction, OCR tooling). Production incident management and on-call experience. Testing strategies for non-deterministic LLM outputs (e.g., golden datasets, fuzzy matching). Domain knowledge in regulated industries (e.g., healthcare/pharma workflows, regulatory compliance) is a plus. Responsibilities: Build, refine, and use ML Engineering platforms and components; develop and implement scalable backend systems, APIs, and microservices using FastAPI. Implement MLOps including model KPI measurement, tracking, model drift detection, and model feedback loops. Deploy and operationalize ML and Deep Learning models, with a strong focus on LLMs and Generative AI. Integrate Azure OpenAI (GPT-4, GPT-4 Vision) and other LLM providers with proper retry logic and error handling. Maintain up-to-date knowledge of state-of-the-art technologies such as LLMs, GenAI, and transformer architectures. Scale machine learning algorithms to work on massive data sets under strict SLAs. Build and orchestrate model pipelines including feature engineering, inferencing, and continuous model training. Write backend application code in Python and SQL using strong object-oriented principles and asynchronous programming (asyncio, async/await). Implement dependency injection patterns and layered architecture (Service, Foundation, Orchestration, DAL