Generative AI Engineer

Job Description:

Role Overview

We are seeking a Generative AI Engineer with 3–5 years of experience to design, develop, and deploy cutting-edge AI solutions powered by Large Language Models (LLMs) and multimodal systems.

The ideal candidate will build scalable GenAI applications such as copilots, chatbots, and intelligent automation systems, leveraging modern frameworks and enterprise data.

GenAI Engineers typically focus on building LLM-powered applications, RAG pipelines, and AI agents, ensuring scalability, reliability, and real-world usability.

Key Responsibilities

• Design, develop, and deploy LLM-powered applications (chatbots, copilots, document intelligence systems)
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge use cases
• Develop AI agents and autonomous workflows using modern frameworks
• Perform prompt engineering and evaluation to improve output quality
• Integrate GenAI models with enterprise systems, APIs, and data sources
• Fine-tune or adapt foundation models (e.g., GPT, LLaMA, multimodal models, SLM's)
• Build scalable pipelines covering data ingestion, embeddings, retrieval, and generation
• Implement LLMOps practices (monitoring, evaluation, versioning, cost optimization)
• Ensure security, governance, and responsible AI practices
• Collaborate with product, data, and platform teams to deliver business solutions

These responsibilities reflect real-world roles involving RAG pipelines, agent workflows, and production-grade GenAI systems.

Required Skills & Qualifications

Education

• Bachelor’s or master’s degree in computer science, AI, Data Science, or related field

Experience

• 3–5 years of experience in software/ML engineering
• At least 1–2 years of hands-on experience in Generative AI / LLM-based systems

Technical Skills

• Strong programming skills in Python
• Experience with LLMs and GenAI frameworks (OpenAI, Hugging Face, Anthropic, Google etc.)
• Hands-on experience with:
• Prompt engineering
• Embeddings & vector databases (FAISS, Pinecone, Qdrant, etc.)
• RAG architecture
• Experience with frameworks like LangChain, LlamaIndex, ADK, or similar
• Knowledge of deep learning frameworks (PyTorch, TensorFlow)
• Experience with API development and microservices
• Familiarity with cloud platforms (AWS, Azure, GCP)
• Understanding of MLOps / LLMOps practices

Modern GenAI roles emphasize LLM integration, prompt workflows, and enterprise data pipelines.

Preferred Skills (Good to Have)

• Experience with agentic AI frameworks (LangGraph, CrewAI, ADK, etc.)
• Knowledge of multimodal AI (text, image)
• Experience in fine-tuning or adapting foundation models
• Familiarity with knowledge graphs or semantic search systems
• Experience building AI copilots or automation systems
• Exposure to UI frameworks (Streamlit, Gradio) for GenAI apps

Many roles now include agent-based systems and multimodal AI capabilities as differentiators.

Soft Skills

• Strong analytical and problem-solving mindset
• Ability to evaluate and improve AI outputs critically
• Effective communication and collaboration
• Adaptability in a rapidly evolving AI landscape
• Ownership of end-to-end delivery

What We Offer

• Opportunity to work on cutting-edge GenAI and LLM-based systems
• Exposure to real-world enterprise AI deployments
• Collaborative, innovation-driven environment
• Career growth in advanced AI and agentic systems
• Experience in Edge AI / IoT systems
• Knowledge graph + Neo4j + reasoning systems
• AI agent orchestration (multi-step workflows, commissioning agents)
• Domain-specific AI (Energy, Design, Forecasting, etc.)