Artificial Intelligence Engineer

Your 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.
• Design and maintain Python microservices using Django or FastAPI with REST/GraphQL APIs.
• Implement real-time communication with Django Channels and 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 Django or 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).
• Hands-on with Docker, Kubernetes basics, and AWS cloud services.
• 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.