Artificial Intelligence Engineer

Python Developer - AI/MLYour Responsibilities- Develop, train, and optimize ML models using PyTorch, TensorFlow, and Keras. - Build end-to-end LLM and RAG pipelines using LangChain and LangGraph. - Work with LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) and implement prompt engineering strategies. - Utilize Hugging Face Transformers for model fine-tuning and deployment. - Integrate embedding models for semantic search and retrieval systems. - Work with transformer-based architectures (BERT, GPT, LLaMA, Mistral) for production use cases. - Implement LLM evaluation frameworks (RAGAS, LangSmith) and performance optimization. - Implement real-time communication with FastAPI WebSockets. - Implement pgvector for embedding storage and similarity search with efficient indexing strategies. - Integrate vector databases (pgvector, Pinecone, Weaviate, FAISS, Milvus) for retrieval pipelines. - Containerize AI services with Docker and deploy on Kubernetes (EKS/GKE/AKS). - Configure AWS infrastructure (EC2, S3, RDS, SageMaker, Lambda, CloudWatch) for AI/ML workloads. - Version ML experiments using MLflow, Weights & Biases, or Neptune. - Deploy models using serving frameworks (TorchServe, BentoML, TensorFlow Serving). - Implement model monitoring, drift detection, and automated retraining pipelines. - Build CI/CD pipelines for automated testing and deployment with ≥80% test coverage (pytest). - Follow security best practices for AI systems (prompt injection prevention, data privacy, API key management). - Participate in code reviews, tech talks, and AI learning sessions. - Follow Agile/Scrum methodologies and Git best practices.Required Qualifications- Bachelor's or Master's degree in Computer Science, AI/ML, or related field. - 2–5 years of Python development experience (Python 3.9+) with strong AI/ML background. - Hands-on experience with LangChain and LangGraph for building LLM-powered workflows and RAG systems. - Deep learning experience with PyTorch or TensorFlow. - Experience with Hugging Face Transformers and model fine-tuning. - Proficiency with LLM APIs (OpenAI, Anthropic, Azure OpenAI) and prompt engineering. - Strong experience with FastAPI frameworks. - Proficiency in PostgreSQL with pgvector extension for embedding storage and similarity search. - Experience with vector databases (pgvector, Pinecone, Weaviate, FAISS, or Milvus). - Experience with model versioning tools (MLflow, Weights & Biases, or Neptune). - Skilled in Git workflows, automated testing (pytest), and CI/CD practices. - Understanding of security principles for AI systems. - Excellent communication and analytical thinking.Nice to Have- Experience with multiple vector databases (Pinecone, Weaviate, FAISS, Milvus). - Knowledge of advanced LLM fine-tuning (LoRA, QLoRA, PEFT) and RLHF. - Experience with model serving frameworks and distributed training. - Familiarity with workflow orchestration tools (Airflow, Prefect, Dagster). - Knowledge of quantization and model compression techniques. - Experience with infrastructure as code (Terraform, CloudFormation). - Familiarity with data versioning tools (DVC) and AutoML. - Experience with Streamlit or Gradio for ML demos. - Background in statistics, optimization, or applied mathematics. - Contributions to AI/ML or LangChain/LangGraph open-source projects.

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