Job Description

Job description:

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About the Role:

We are seeking a skilled GenAI Engineer with approximately 3 years of experience to join our dynamic team. You will play a crucial role in developing and deploying cutting-edge AI-driven solutions, working directly with our AI/GenAI Architect to implement advanced multi-agent systems and conversational AI for enterprise applications. This is an opportunity to be at the forefront of AI innovation, building solutions that deliver real-world impact.

What You'll Do

Engineering & Implementation: Develop and deploy conversational agents, context-sensitive document handlers, and automated content workflows using Large Language Models (LLMs). Context Engineering: Design and implement strategies for managing and manipulating data context to improve model performance and relevance in our applications. Multi-Agent Systems Development: Implement and integrate components of multi-agent systems, focusing on effective agent orchestration, modular workflows, and seamless agent-to-agent interactions. System Integration & Pipelines: Build robust pipelines for knowledge retrieval (RAG), embedding services, and secure, scalable connectors to integrate with enterprise systems. Monitoring & Observability: Implement logging, tracing, and monitoring to ensure the reliability and optimal performance of our AI systems.

What We're Looking For

Bachelor's degree in Computer Science, AI, Data Science, or a related field. Around 2 years of professional experience in AI/ML engineering, with a focus on Generative AI and LLM technologies. Strong problem-solving skills and an analytical mindset. Excellent communication skills and the ability to work collaboratively in an agile environment. A passion for staying updated on emerging AI/GenAI technologies and trends.

Required Technical Proficiencies

Programming & Frameworks: Proficiency in Python and its core data science libraries.

Generative AI & LLMs:

Practical experience with prompt engineering and advanced fine-tuning techniques. Experience with context engineering to manage and enhance model context. Understanding of Retrieval-Augmented Generation (RAG) architectures and their implementation. Experience with multi-agent orchestration frameworks like LangGraph, Agno, AutoGen, or CrewAI. Familiarity with LLM application frameworks such as LangChain or LlamaIndex.

Databases & Storage:

Experience with vector databases for RAG implementations (Pinecone, Milvus, ChromaDB, or others). Knowledge of traditional and non-traditional data storage solutions. Cloud & Deployment: Experience building and deploying GenAI agents using cloud-native tools on AWS (e.g., Amazon Bedrock Agents) or Azure (e.g., Azure AI Foundry Agent Service).

Good to Have

Proficiency in containerization and orchestration tools like Docker and Kubernetes. Experience with Infrastructure-as-Code (IaC) using Terraform. Familiarity with foundational AI/ML frameworks like PyTorch or TensorFlow.

Education - BE/B.Tech/ME/M.Tech/MCA/MSc (Computers)

Interview rounds - 3 Technical rounds

Experience level: 2-5 Years

If you're interested in this opportunity, we'd be happy to receive your updated CV. Simply click the email link below, or copy and paste the email address:

careers@innominds.com

or rvadde@innominds.com