Gen AI Engineer

Role Overview

You will be involved in full lifecycle AI solution delivery – from research and prototyping to scaling and deployment. The ideal candidate combines strong theoretical ML/DL grounding with applied experience in foundation models, LLM fine-tuning, vector search, Gen AI application frameworks, and deployment on large-scale infrastructure.

Key Responsibilities

· Architect, build, and deploy Generative AI solutions tailored to business problems.

· Fine-tune and customize foundation/LLM models (e.g., GPT, LLaMA, Mistral, Falcon, Claude, Gemma).

· Build LLM-powered applications using frameworks such as LangChain, Haystack, LlamaIndex.

· Develop and manage RAG (Retrieval Augmented Generation) pipelines integrating vector databases (FAISS, Pinecone, Weaviate, Milvus, ChromaDB).

· Work with cloud AI services such as Azure OpenAI, AWS Bedrock, SageMaker, GCP Vertex AI.

· Implement MLOps pipelines for model training/monitoring using tools like MLflow, Kubeflow, Weights & Biases, DVC.

· Leverage Hugging Face ecosystem (Transformers, Diffusers, PEFT, Datasets) for model experimentation.

· Optimize AI workflows with GPU acceleration and inference optimization (e.g., ONNX, TensorRT, DeepSpeed, vLLM).

· Design and enforce secure, ethical, and responsible AI practices in all deployments.

· Collaborate with consultants, data engineers, and business analysts to understand client problems and deliver measurable solutions.

· Mentor junior engineers; contribute to internal accelerators and reusable solution templates.

Requirements

Required Qualifications

· Education: Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, or a related field.

· Experience: 5+ years in ML/DL, with minimum 2 years in Generative AI solution development.

· Expertise with Python and ML/DL libraries (PyTorch, TensorFlow, JAX).

· Strong knowledge of LLM training/fine-tuning techniques: LoRA, QLoRA, PEFT, instruction tuning.

· Proficiency in prompt engineering and evaluation of model outputs.

· Hands-on with vector databases and indexing pipelines for semantic search.

· Familiar with containerization and deployment tools (Docker, Kubernetes, Helm).

· Exposure to CI/CD pipelines for AI solutions and cloud-native patterns.

Preferred Skills

· Experience in enterprise AI implementation (chatbots, document intelligence, knowledge assistants, customer interaction systems).

· Exposure to multimodal AI (e.g., CLIP, Stable Diffusion, DALL·E, Whisper).

· Contributions to open-source or personal projects showcasing Gen AI apps.

· Experience in reinforcement learning (RLHF/DPO) for model alignment.

· Working knowledge of streaming and event-driven architectures (Kafka, Flink) for real-time AI applications.

Benefits