Senior Gen AI Engineer

Job Overview:

We are looking for a Senior Gen AI Engineer with proven expertise in building robust, scalable, and secure backend systems for AI, Generative AI (GenAI), and Data Science applications. The ideal candidate will combine strong backend engineering fundamentals with hands-on experience integrating AI/ML models, optimizing data pipelines, and enabling enterprise-grade deployments. This role is highly collaborative, requiring close work with data scientists, AI architects, and product teams to deliver high-performance solutions.

Key Responsibilities:

• Design, develop, and maintain scalable backend services and APIs in Python (FastAPI, Flask, Django).
• Build data ingestion, transformation, and feature engineering pipelines for AI/ML and GenAI.
• Implement microservices with reliability, observability, and maintainability.
• Optimize backend systems for performance, cost, and scalability in cloud environments.
• Work with data scientists to productionize ML/GenAI models (deployment, monitoring, lifecycle).
• Integrate LLMs, vector databases, embeddings, and RAG pipelines into services.
• Develop APIs and SDKs to expose AI/ML capabilities.
• Apply responsible AI practices: bias detection, security, compliance.
• Translate business needs into technical designs with architects and product teams.
• Mentor junior engineers, enforce best practices, and support CI/CD automation.
• Contribute to design discussions, architecture reviews, and sprint planning.

Key Skills:

• 6+ years of backend engineering experience with Python in enterprise or product environments.
• Strong expertise in FastAPI, Flask, Django, or similar frameworks.
• Proven experience working on AI/ML/GenAI projects (model deployment, serving, integration).
• Hands-on with data processing frameworks (Pandas, PySpark, Dask) and ML frameworks (PyTorch, TensorFlow, Hugging Face).
• Familiarity with vector databases (Pinecone, Weaviate, FAISS, Milvus) and orchestration tools (Airflow, Dagster, Prefect).
• Strong understanding of REST/gRPC APIs, microservices, containerization (Docker, Kubernetes), and CI/CD pipelines.
• Cloud experience with AWS, Azure, or GCP (serverless, Kubernetes, AI/ML services).
• Solid understanding of security, scalability, and performance tuning in backend systems.

Preferred Qualifications

• Experience with LLMOps/ MLOps (MLflow, Weights & Biases, LangChain/LangGraph).
• Exposure to multi-agent systems, advanced RAG, or AI governance patterns.
• Prior experience in data-intensive applications (streaming, big data pipelines).
• Open-source contributions or publications in AI/ML/GenAI.