Pyhton gen ai developer

Pyhton Gen AI Developer

Your Responsibilities

Develop, train, and optimize ML models using Py Torch, Tensor Flow, and Keras.

Build end-to-end LLM and RAG pipelines using Lang Chain and Lang Graph.

Work with LLM APIs (Open AI, Anthropic Claude, Azure Open AI) 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, LLa MA, Mistral) for production use cases.

Implement LLM evaluation frameworks (RAGAS, Lang Smith) and performance optimization.

Implement real-time communication with Fast API Web Sockets.

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, Sage Maker, Lambda, Cloud Watch) for AI / ML workloads.

Version ML experiments using MLflow, Weights & Biases, or Neptune.

Deploy models using serving frameworks (Torch Serve, Bento ML, Tensor Flow 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 Lang Chain and Lang Graph for building LLM-powered workflows and RAG systems.

Deep learning experience with Py Torch or Tensor Flow.

Experience with Hugging Face Transformers and model fine-tuning.

Proficiency with LLM APIs (Open AI, Anthropic, Azure Open AI) and prompt engineering.

Strong experience with Fast API frameworks.

Proficiency in Postgre SQL 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 (Lo RA, QLo RA, 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, Cloud Formation).

Familiarity with data versioning tools (DVC) and Auto ML.

Experience with Streamlit or Gradio for ML demos.

Background in statistics, optimization, or applied mathematics.

Contributions to AI / ML or Lang Chain / Lang Graph open-source projects.