Generative AI Engineer
60 minutes ago
Role Overview:
Looking for a Generative AI Specialist with 5-7 years of experience focusing on state-of-the-art generative AI technologies including large language models (LLMs) and agentic AI. The candidate will act as a technical mentor, engaging with clients to develop advanced generative solutions and mentor junior team members.
Key Responsibilities:
- Architect, fine-tune, and deploy Generative AI and LLM-based solutions for diverse industries.
- Lead development of RAG pipelines, multi-agent orchestration, and generative workflows for automation.
- Collaborate with clients to define problem statements, data strategies, and AI integration approaches.
- Design and execute rapid prototypes and POCs using transformer-based and diffusion-based architectures.
- Mentor junior engineers on LLM fine-tuning, prompt engineering, and API-based integration.
- Continuously evaluate and adopt emerging open-source frameworks and foundation models (e.g., Llama, Claude, Gemini, Mistral).
- Drive MLOps integration for generative AI solutions ensuring scalability, reproducibility, and compliance.
Requirements:
- 5–7 years of overall AI experience, with at least 2–3 years dedicated to Generative AI and LLM-based systems.
- Hands-on experience building RAG, conversational, or multi-agent-based AI architectures.
- Expertise in fine-tuning, prompt optimization, and LLM orchestration.
- Proven success in developing generative solutions for enterprise applications.
- Excellent communication and technical mentoring skills.
- Bachelor’s/master’s degree in computer science, AI, or related discipline
Desired Technical Skills & Tools:
- Programming: Python, LangChain, LlamaIndex, Multi Agentic Systems
- LLM Frameworks: OpenAI GPT series, Hugging Face Transformers, Anthropic Claude, Mistral, Llama 3, Agnos
- Generative AI Tools: LangGraph, Vector Databases (Pinecone, FAISS, Chroma), PromptLayer, LangFuse
- RAG & Agent Frameworks: Haystack, Semantic Kernel, AutoGen, CrewAI
- MLOps & Deployment: Docker, Kubernetes, MLflow, Vertex AI, Azure OpenAI, AWS Bedrock
- Data & Integration: REST APIs, Kafka, ElasticSearch, PostgreSQL, NoSQL, Data Lakes
- Other Skills: Prompt Engineering, Model Evaluation Metrics, Hallucination Control, Knowledge Graphs, Context Management
- Cloud & Infra: Azure AI Studio, AWS SageMaker, GCP GenAI Studio
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