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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 up-to-date 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.)

📌 Generative AI Engineer (Bengaluru)
🏢 Mancraft Consulting
📍 Bengaluru