Generative AI Engineer

Generative AI Engineer

We are seeking an innovative Generative AI Engineer to design, develop, and deploy AI-powered solutions using Large Language Models (LLMs), Generative AI frameworks, and advanced machine learning techniques. The ideal candidate will be responsible for building intelligent applications such as AI assistants, chatbots, content generation systems, recommendation engines, and enterprise AI solutions. This role requires close collaboration with Data Scientists, Software Engineers, Product Managers, and MLOps teams to deliver scalable and secure AI products.

Key Responsibilities:

• Design, develop, and deploy Generative AI applications using modern AI frameworks and LLMs.
• Build AI-powered chatbots, virtual assistants, content generation systems, and automation solutions.
• Develop and optimize prompt engineering strategies to improve AI model performance.
• Implement Retrieval-Augmented Generation (RAG) architectures using vector databases.
• Fine-tune and evaluate foundation models and open-source LLMs.
• Integrate AI models with enterprise applications, APIs, and cloud platforms.
• Develop data pipelines for training, inference, and model evaluation.
• Monitor AI model performance, accuracy, and operational reliability.
• Implement responsible AI practices, security controls, and governance standards.
• Collaborate with MLOps teams to automate model deployment and monitoring.
• Stay updated with emerging AI technologies, research, and industry trends.

Required Skills:

• Strong understanding of Generative AI, Machine Learning, and Deep Learning concepts.
• Experience working with Large Language Models (LLMs) and Foundation Models.
• Expertise in Prompt Engineering and AI application development.
• Knowledge of Retrieval-Augmented Generation (RAG) architectures.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.

Technical Skills:

• Programming Languages: Python, JavaScript, SQL
• AI Frameworks: LangChain, LlamaIndex, Haystack
• Machine Learning Frameworks: TensorFlow, PyTorch, Hugging Face Transformers
• LLM Platforms: OpenAI, Anthropic Claude, Gemini, Mistral, Llama
• Vector Databases: Pinecone, Weaviate, ChromaDB, Milvus
• Cloud Platforms: AWS, Azure, Google Cloud Platform (GCP)
• APIs & Integrations: REST APIs, GraphQL
• Containerization: Docker, Kubernetes
• Version Control: Git, GitHub, GitLab
• Monitoring Tools: LangSmith, Weights & Biases, MLflow

Qualifications:

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field.
• Master's degree is a plus.
• Relevant certifications in AI, Cloud, or Machine Learning are preferred.

Experience:

• 3–8 years of experience in Software Engineering, Machine Learning Engineering, AI Engineering, or Data Science.
• Hands-on experience building and deploying Generative AI solutions.
• Experience working with LLM APIs and open-source language models.
• Experience with cloud-native AI application development.

Preferred Qualifications:

• Experience fine-tuning LLMs using techniques such as LoRA, PEFT, and RLHF.
• Knowledge of AI Agents, Agentic Workflows, and Multi-Agent Systems.
• Experience with AI model evaluation, hallucination detection, and guardrail implementation.
• Understanding of Responsible AI, AI Governance, and AI Security.
• Experience building enterprise-grade AI platforms and knowledge management systems.

Preferred Qualities:

• Strong innovation and research mindset.
• Passion for emerging AI technologies and experimentation.
• Excellent troubleshooting and optimization skills.
• Ability to work in a fast-paced and evolving technology landscape.
• Strong ownership and accountability for AI product quality.

Employment Type:

Full-Time

Location:

Remote / Hybrid / On-site

Nice to Have:

• Experience with Agentic AI frameworks such as CrewAI, AutoGen, LangGraph, or Semantic Kernel.
• Knowledge of AI-powered testing, automation, and quality engineering.
• Experience developing AI copilots, enterprise search solutions, and knowledge assistants.
• Familiarity with MLOps and AIOps practices.
• Contributions to open-source AI projects, research publications, or AI communities.