Data Scientist

Job Summary

As a Generative AI Engineer at Cyient, you will play a pivotal role in designing, developing, and deploying enterprise-scale AI solutions powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and Agentic AI frameworks. You will work closely with cross-functional teams to build intelligent applications that automate workflows, enhance decision-making, and accelerate digital transformation across engineering and enterprise domains.

You will leverage cutting-edge AI technologies, cloud platforms, and backend engineering principles to deliver scalable, secure, and production-ready AI systems.

Job Responsibilities

• Design, develop, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs) such as GPT, Claude, and open-source models.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding generation, vector indexing, and hybrid search.
• Develop AI Agents and multi-agent workflows using frameworks such as LangChain, LangGraph, Microsoft Agent Framework, or similar orchestration platforms.
• Integrate enterprise applications and external tools using Model Context Protocol (MCP), function calling, and API orchestration.
• Fine-tune open-source LLMs using LoRA, QLoRA, PEFT, and Hugging Face Transformers to improve domain-specific performance.
• Design scalable backend services and REST APIs using Python (FastAPI) and/or Java Spring Boot to support AI applications.
• Deploy AI workloads on Azure, AWS, or GCP using Docker, Kubernetes, and CI/CD pipelines.
• Build and maintain vector databases, semantic search, and hybrid retrieval systems using FAISS, Pinecone, Chroma, Weaviate, or Azure AI Search.
• Implement LLM evaluation and monitoring using frameworks such as LangSmith, RAGAS, DeepEval, MLflow, and Prometheus/Grafana.
• Collaborate with product managers, solution architects, data engineers, and domain experts to translate business requirements into AI-powered solutions.
• Optimize AI applications for latency, scalability, security, and production reliability.
• Stay current with advancements in Generative AI, Agentic AI, multimodal AI, and emerging LLM technologies, and evaluate their applicability to enterprise use cases.
• Contribute to AI architecture, best practices, reusable frameworks, and coding standards across the organization.
• Document AI solutions, deployment architectures, and technical designs while mentoring junior engineers where applicable.

Job Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Electronics, Information Technology, or a related engineering discipline.
• 6+ years of professional experience in Generative AI, AI/ML, or backend software engineering.
• Strong proficiency in Python and/or Java, with experience developing scalable backend applications.
• Hands-on experience with Large Language Models (LLMs) and enterprise AI application development.
• Strong knowledge of LangChain, LangGraph, AI Agents, Agentic AI, Prompt Engineering, Context Engineering, and RAG architectures.
• Experience with OpenAI API, Azure OpenAI Service, Anthropic Claude API, Hugging Face Transformers, or similar LLM platforms.
• Practical experience in LoRA, QLoRA, PEFT, model fine-tuning, quantization, and local model deployment using vLLM or similar inference engines.
• Experience with vector databases such as FAISS, Chroma, Pinecone, Weaviate, or Azure AI Search.
• Strong understanding of embeddings, semantic search, hybrid search, document processing, and retrieval optimization.
• Experience building REST APIs using FastAPI, Spring Boot, or similar frameworks.
• Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform.
• Hands-on experience with Docker, Kubernetes, Git, and CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps).
• Familiarity with event-driven architectures using Kafka or similar messaging platforms.
• Experience implementing LLM observability, tracing, evaluation, and monitoring using LangSmith, RAGAS, DeepEval, MLflow, Prometheus, and Grafana.
• Good understanding of software engineering principles, microservices architecture, API security (OAuth2/JWT), and scalable system design.