Software Engineer Generative AI Projects

Role Overview

We are seeking a Software Engineer specializing in Generative AI (GenAI) to design, develop, and deploy innovative AI-driven applications leveraging Large Language Models (LLMs) and Azure OpenAI.

The ideal candidate has strong Python development experience, a solid understanding of AI/ML concepts, and hands-on exposure to containerized environments (Docker, Kubernetes, AKS). You’ll work on building scalable APIs and systems that integrate AI capabilities into real-world products.

Key Responsibilities

• Design, build, and deploy AI-powered applications leveraging Generative AI models (LLMs, diffusion models, embeddings, etc.).
• Develop and maintain Python-based services for AI/ML workflows and APIs.
• Integrate Azure OpenAI, Hugging Face, or other LLM APIs into enterprise applications.
• Build RESTful APIs for serving AI models and managing inference pipelines.
• Collaborate with data scientists, ML engineers, and DevOps to operationalize and scale AI models.
• Containerize AI workloads using Docker and orchestrate deployments on Kubernetes / AKS.
• Optimize model serving performance, latency, and scalability.
• Implement MLOps best practices for CI/CD, model versioning, and monitoring.
• Conduct research and prototyping on emerging GenAI technologies, model fine-tuning, and prompt engineering.
• Ensure security, compliance, and reliability in AI solution delivery.

Technical Skills Required

Must Have:

• Strong proficiency in Python for AI/ML development.
• Solid understanding of Machine Learning fundamentals (data preprocessing, model training, evaluation).
• Experience with Generative AI / LLMs (OpenAI GPT, Azure OpenAI, Hugging Face Transformers, LangChain, etc.).
• Hands-on experience with Docker, Kubernetes, and Azure Kubernetes Service (AKS).
• Experience developing RESTful APIs using frameworks like FastAPI, Flask, or Django.
• Familiarity with Azure cloud services related to AI (Azure ML, Azure OpenAI, Cognitive Services).

Nice to Have:

• Knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), and vector databases (Pinecone, FAISS, ChromaDB).
• Experience with CI/CD pipelines and MLOps tools (MLflow, DVC, GitHub Actions, Azure DevOps).
• Understanding of data pipelines, feature stores, and model monitoring.
• Exposure to NLP, text generation, image generation, or multi-modal AI systems.