Gen AI Developer

Key Responsibilities

• Design and implement scalable AI applications, advanced Retrieval-Augmented Generation (RAG) pipelines, and multi-agent systems.
• Write clean, optimized, and maintainable Python code using frameworks like FastAPI or Flask to expose AI capabilities via robust APIs.
• Apply traditional machine learning techniques for data preprocessing, feature engineering, embeddings optimization, and model evaluation/fine-tuning.
• Architect and manage cloud infrastructure (AWS/Azure/GCP) to deploy, monitor, and scale AI workloads using Docker, Kubernetes, or serverless services.
• Manage high-performance vector databases (e.g., Pinecone, Milvus, Qdrant) and design efficient data ingestion pipelines.
• Mentor junior engineers, conduct code reviews, define engineering best practices, and collaborate with cross-functional product teams.

Required Skills & Qualifications

• Expert-level proficiency in Python and standard backend development patterns.
• Deep hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, or Haystack) and commercial/open-source models (OpenAI, Anthropic, Llama).
• Solid understanding of ML fundamentals, evaluation metrics, and frameworks like PyTorch, Hugging Face, or Scikit-Learn.
• Experience deploying applications on AWS (SageMaker, Bedrock, ECS), Azure (OpenAI Service, AKS), or GCP (Vertex AI, GKE).
• Strong knowledge of both traditional databases (SQL/NoSQL) and vector databases.
• Familiarity with CI/CD pipelines, containerization (Docker), and model monitoring tools.

Preferred Qualifications

• Experience with fine-tuning open-source LLMs or optimizing context windows.
• Understanding of AI safety, guardrails, and cost-optimization strategies for LLM tokens.