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.