Overview
We are seeking a motivated Generative AI Engineer with 1–3 years of experience in Machine Learning, Generative AI, and Large Language Models (LLMs). The ideal candidate should have strong programming skills in Python, a solid understanding of ML/AI fundamentals, and hands-on experience building GenAI applications using LLMs, Retrieval-Augmented Generation (RAG), and AI agent frameworks.
The candidate will work closely with senior engineers, data scientists, and cross-functional teams to design, develop, test, and deploy scalable GenAI solutions for real-world business problems. This role offers an opportunity to work on emerging technologies, including Agentic AI, multi-agent systems, LangGraph, and open-source LLMs.
Duties and Responsibilities
- Develop and implement Generative AI applications using LLMs and modern AI frameworks.
- Build and maintain RAG pipelines using document processing, embeddings, vector databases, and semantic search.
- Implement AI agents and workflow orchestration using frameworks such as LangChain and LangGraph.
- Work with LLM APIs, including OpenAI APIs and cloud-based AI services.
- Explore and integrate open-source models such as Llama, Mistral, and other Hugging Face models.
- Perform prompt engineering and optimize prompts to improve response quality, accuracy, and consistency.
- Assist in fine-tuning, evaluating, and optimizing LLMs for specific business use cases.
- Develop Python scripts and APIs to integrate GenAI solutions with existing applications and enterprise systems.
- Implement basic evaluation frameworks to assess LLM responses, RAG retrieval quality, relevance, and hallucination.
- Optimize application performance by reducing response latency, token consumption, and computational costs.
- Deploy and support GenAI applications on cloud platforms such as Microsoft Azure or AWS.
- Follow software development best practices, including version control, testing, debugging, and code documentation.
- Collaborate with senior engineers and business teams to understand requirements and translate them into AI-powered solutions.
- Stay updated with advancements in Generative AI, LLMs, Agentic AI, and emerging AI frameworks.
Requirements
- B.Tech/M.Tech in Computer Science, Information Technology, Artificial Intelligence, or a related field.
- 1–3 years of hands-on experience in Machine Learning, AI, Data Science, or Generative AI development.
- Strong programming skills in Python and familiarity with libraries such as Pandas, NumPy, and Scikit-learn.
- Basic to intermediate understanding of Machine Learning, Deep Learning, NLP, and neural network architectures.
- Hands-on experience with LLMs, Generative AI applications, and prompt engineering.
- Practical knowledge of RAG architecture, embeddings, semantic search, and vector databases.
- Familiarity with frameworks such as LangChain, LangGraph, or Hugging Face Transformers.
- Experience integrating LLMs through OpenAI APIs or other commercial/open-source model APIs.
- Familiarity with vector databases or search technologies such as FAISS, Chroma, Pinecone, or Azure AI Search.
- Basic understanding of REST APIs, FastAPI or Flask, and application integration.
- Familiarity with Git, Docker, and software development practices is preferred.
- Exposure to cloud platforms such as Microsoft Azure or AWS is an advantage.
- Strong problem-solving skills, willingness to learn, and the ability to work collaboratively in a team.
Good to Have
- Exposure to Agentic AI workflows, tool calling, and multi-agent systems.
- Basic hands-on experience with LangGraph for stateful AI workflows.
- Familiarity with open-source LLMs such as Llama, Mistral, and similar models.
- Understanding of LLM evaluation, guardrails, hallucination mitigation, and responsible AI practices.
- Exposure to model fine-tuning, quantization, or inference optimization.
- Familiarity with MLflow or other tools for experiment tracking and model management.
Key Skills
Core: Python, Machine Learning, Generative AI, LLMs, NLP
GenAI: RAG, Prompt Engineering, Embeddings, Vector Databases, LLM Evaluation
Frameworks: LangChain, LangGraph, Hugging Face, OpenAI APIs
Deployment: FastAPI, REST APIs, Git, Docker, Azure/AWS
What We Are Looking For
A proactive and curious engineer who enjoys solving real-world problems using Generative AI, can build practical AI applications, and is eager to grow their expertise in Agentic AI, multi-agent orchestration, and scalable enterprise AI solutions.
