Technical Architect

4 days ago

Kurnool, Andhra Pradesh, India beBeeTechnical Full time ₹ 15,00,000 - ₹ 20,10,000

Job Overview

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As a key member of our team, you will be responsible for spearheading the development and integration of Python-based applications with Large Language Models (LLMs).

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Key Responsibilities

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  • Design, implement, and integrate LLM pipelines, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and evaluation.
  • Develop scalable microservices and APIs for AI features, ensuring performance optimization and security.
  • Collaborate with MLOps teams to deploy and monitor AI models in production, maintaining cost efficiency and adhering to security standards.
  • Provide technical guidance on Python best practices, code reviews, and problem-solving, fostering a culture of continuous learning.
  • Stay updated on emerging AI/LLM advancements and propose adoption where beneficial, driving innovation within the organization.
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Mandatory Skills and Qualifications

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  • Strong proficiency in Python, specifically FastAPI and Flask.
  • Significant experience with LLM integration, utilizing OpenAI API, Hugging Face Transformers, LangChain, and LlamaIndex.
  • Understanding of RAG pipelines and vector databases like Pinecone, Weaviate, FAISS, and Milvus.
  • Experience with prompt engineering & evaluation techniques and knowledge of MLOps tools such as MLflow, Kubeflow, and Langfuse, with deployment capabilities on AWS/GCP/Azure.
  • Familiarity with containerization and orchestration using Docker and Kubernetes, along with expertise in REST APIs, GraphQL, and microservices architecture.
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Desirable Skills and Experience

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  • Excellent leadership and mentoring abilities, with strong problem-solving and analytical skills.
  • Effective communication with technical and non-technical stakeholders, facilitating seamless collaboration.
  • Ability to work in a fast-paced, evolving AI environment, adapting to changing requirements and priorities.
  • Experience with agentic AI or Model Context Protocol (MCP) and background in data pipelines (ETL, streaming data).
  • Exposure to AI security & compliance practices and prior work in scalable enterprise AI products.

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