GYTWorkz Technologies Pvt Ltd
https://gytworkz.comAbout
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Jobs at GYTWorkz Technologies Pvt Ltd
Design and develop Agentic AI systems using LLMs, tools, memory,
workflows, and MCP.
Build production-grade RAG pipelines, including ingestion, chunking,
embeddings, retrieval, reranking, and evaluation.
Implement context engineering strategies for improving LLM accuracy,
relevance, and reliability.
Develop and integrate MCP-based tools and services for AI agents.
Work with LLMs, SLMs, quantized models, and model optimization
techniques for efficient inference.
Develop scalable backend services and APIs for AI applications.
Design databases and data models supporting AI/agentic applications.
Implement AI observability covering latency, token usage, cost, failures,
quality, and agent/tool execution.
Apply AI governance and responsible AI practices, including security,
access control, data privacy, and auditability.
Optimize AI systems for latency, scalability, cost, and reliability.
Collaborate with engineering and product teams to take AI solutions from
POC to production.
Strong hands-on experience with GenAI, LLMs, and Agentic AI.
Experience building RAG applications.
Strong understanding of Context Engineering and prompt/context
optimization.
Role Overview
We are looking for a hands-on AI/ML Engineer to design, develop, and deploy
production-ready GenAI and Agentic AI applications. The role involves building
intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI
infrastructure with a strong focus on context engineering, observability,
governance, and model optimisation.
Key Responsibilities
Required Skills
Practical experience with MCP (Model Context Protocol).
Experience with frameworks such as LangChain, LangGraph,
LlamaIndex, or equivalent.
Knowledge of LLM/SLM deployment and quantization techniques.
Strong Python backend development experience.
Experience developing REST APIs using FastAPI/Flask or equivalent.
Strong understanding of SQL/NoSQL databases and database design.
Experience with vector databases such as Qdrant, Pinecone, Weaviate,
ChromaDB, or FAISS.
Understanding of AI observability, evaluation, monitoring, and
governance.
Experience with cloud platforms and production deployment is preferred.
Strong understanding of software engineering principles, Git, testing, and
CI/CD.
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