AI/ML Engineer - OpenAI, Fast API, Numpy , Panda, 3+ yrs - Bangalore

• Develop and maintain high-performance FastAPI/Flask microservices for AI-driven products
• Integrate AI/ML models into backend APIs for chatbots, RAG systems, and recommendation engines
• Implement secure RESTful and event-driven APIs with versioning, error handling, and monitoring
• Manage authentication, rate limiting, and audit logging for AI endpoints

AI Integration & Tools

• Integrate cloud AI APIs (OpenAI, Anthropic, Gemini, Groq, etc.) and self-hosted models (Ollama, vLLM)
• Develop SDK wrappers for text, image, video, and voice-based intelligence modules
• Use LangChain, LlamaIndex, and embeddings for retrieval-augmented generation (RAG) workflows
• Implement pipelines for document parsing, summarization, and contextual reasoning

Data & Database Engineering

• Build data ingestion and transformation pipelines using Pandas, NumPy, or Airflow/Prefect
• Integrate and query vector databases (Pinecone, Weaviate, pgvector, Milvus) for embeddings
• Design schemas and optimize queries for PostgreSQL and NoSQL systems supporting AI workloads
• Handle structured/unstructured data (PDFs, audio, text) efficiently for downstream AI tasks

AI Services & Architecture

• Architect scalable, modular components for multimodal AI (text, speech, image)
• Build SDK-based AI services for unified orchestration across multiple providers
• Optimize backend-to-model communication for low latency and high throughput
• Collaborate with frontend and DevOps teams for full-stack integration

MLOps & Model Handling (Optional)

• Basic familiarity with model deployment using Docker, MLflow, or TorchServe
• Support fine-tuning and inference workflows when required
• Exposure to Vertex AI / SageMaker / KServe is a plus

Required Skills

• Programming & Development

• Strong proficiency in Python, OOP principles, and API design
• Experience with FastAPI or Flask microservices
• Understanding of PyTorch or TensorFlow frameworks

• Databases

• Proficiency in PostgreSQL, Redis, and vector databases like Pinecone, Weaviate, pgvector, or Milvus

• AI & Integration Tools

• Experience with LangChain, LlamaIndex, HuggingFace, OpenAI, or similar APIs
• Familiarity with FOSS AI stacks, embeddings, and agentic frameworks (LangGraph, CrewAI, AutoGen)
• Knowledge of MCP and A2A communication protocols

• Architecture & Infrastructure

• Understanding of REST APIs, microservices, and containerized deployments
• Working knowledge of Docker; basic Kubernetes/GPU familiarity preferred.