About the Role

We are looking for an experienced AI Engineer to join our team and help build and scale GenAI-powered products. The ideal candidate has hands-on experience designing, deploying, and optimizing RAG (Retrieval-Augmented Generation) based solutions, working with LLMs, and building agentic AI systems that operate reliably in production environments.

Key Responsibilities

- Design, develop, and deploy RAG pipelines including chunking strategies, embedding generation, vector database integration, and retrieval optimization.
- Build and fine-tune LLM-based applications, including prompt engineering, evaluation, and guardrails for accuracy and safety.
- Architect and implement agentic AI workflows — multi-step reasoning, tool-calling, memory management, and orchestration across agents.
- Integrate LLM/agentic solutions with internal and external APIs, databases, and business systems.
- Optimize model performance, latency, and cost across inference pipelines.
- Collaborate with Product, Data Engineering, and Analytics teams to translate business requirements into scalable AI solutions.
- Implement evaluation frameworks to monitor hallucination rates, retrieval accuracy, and response quality.
- Stay current with the evolving LLM/GenAI ecosystem (new models, frameworks, and techniques) and evaluate their applicability.




- Ensure solutions are production-ready — including logging, monitoring, versioning, and error handling.
- Document architecture, workflows, and technical decisions for internal knowledge sharing.

Required Skills & Experience

- 5 years of total experience in software/data engineering, with 2–3 years working on AI/ML or GenAI solutions in a similar industry.
- Proven experience building RAG-based solutions end-to-end (retrieval, indexing, chunking, embeddings, vector search).
- Strong hands-on experience working with LLMs (OpenAI, Anthropic, open-source models, etc.) — prompt engineering, fine-tuning, and evaluation.
- Practical experience building agentic AI systems — tool use, multi-agent orchestration, task planning/execution loops.
- Proficiency in Python and common AI/ML frameworks (e.g., LangChain, LlamaIndex, Hugging Face, or similar).
- Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus, or similar).
- Solid understanding of API design and integration for AI-powered applications.
- Familiarity with cloud platforms (AWS/Azure/GCP) for deploying AI workloads.
- Strong understanding of software engineering fundamentals — version control, testing, CI/CD.
- Good problem-solving skills with the ability to work in a fast-paced, cross-functional setting.

📌 AI Engineer (Haryana)
🏢 Easyrewardz Software Services
📍 Haryana