Gen AI Engineer

Role & responsibilities

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.

Preferred candidate profile

Required Qualifications

• Education: Bachelors/Masters 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.

What We Offer

• Opportunity to work on cutting-edge AI projects across industries and domains.
• Exposure to industry-grade Gen AI stacks from multiple providers.
• A flat, collaborative environment where innovation and ownership are valued.
• Competitive compensation and career growth as part of a scaling consulting team.
• Support for continuous upskilling (certifications, conferences, workshops).