GEN AI Engineer

Key Responsibilities

• End-to-End Pipeline Development: Design, build, and maintain robust, scalable, and efficient data and model pipelines for generative AI applications, encompassing data ingestion, preprocessing, training, fine-tuning, and inference.
  

• Model Training & Fine-Tuning: Develop, train, and fine-tune state-of-the-art generative models (LLMs, LVMs, etc.) using advanced techniques such as Parameter-Efficient Fine-Tuning (PEFT/P-Tuning/LoRA) and Reinforcement Learning from Human Feedback (RLHF).
  

• Retrieval-Augmented Generation (RAG): Architect, implement, and optimize RAG systems by integrating vector databases (e.g., Pinecone, Milvus, Weaviate, pgvector) to enhance model accuracy and reduce hallucinations.
  

• Model Deployment: Package, containerize, and deploy models into production using modern MLOps and deployment tools like AWS SageMaker, Kubernetes, or similar platforms, ensuring reliability and scalability.
  

• Multi-Modal & Agentic Development: Research and prototype multi-modal (text, image, audio) AI solutions and develop agentic systems capable of planning and executing complex tasks.
  

Qualifications (Required)

• Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
  

• Experience: 2-3 years of professional software development experience with a strong focus on AI/ML.
  

• Design and implement scalable backend services using frameworks (Django/Flask/FastAPI)
  

• Programming: Expert proficiency in Python and strong experience with AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers).
  

• Generative AI Fundamentals: Proven hands-on experience in at least three of the following:
  

• Prompt Engineering for large language models (LLMs).
  

• Building applications using open-source models from HuggingFace.
  

• Implementing RAG architectures using vector databases.
  

• Fine-tuning models using PEFT methods (e.g., LoRA, Adapters).
  

• Diffusion models, embeddings orchestration
  

• Familiarity with performance profiling, efficient model serving, and hardware-aware design (e.g., GPU utilization, quantization).
  

• Cloud & Deployment: Solid experience deploying and managing models in a cloud environment (AWS, GCP, or Azure). Direct experience with AWS SageMaker is a significant plus.
  

• Pipeline integration knowledge with web apps, for instance, Python and Ruby on Rails web applications
  

• Software Engineering: Strong understanding of software engineering principles, design patterns, and writing production-quality, maintainable code (version control, testing, debugging).
  

Qualifications (Preferred)

• Experience with RLHF and evaluating human preferences for model alignment.
  

• Practical knowledge of multi-modal models (e.g., CLIP, FLAN-T5, Vision Transformers).
  

• Experience building agentic workflows (e.g., using LangChain, LlamaIndex, or custom frameworks).
  

• Familiarity with containerization (Docker) and orchestration (Kubernetes).
  

• Experience with distributed training frameworks (e.g., DeepSpeed, FSDP).