Nilasu Consulting Services Pvt Ltd logo

Jr. AI Engineer(NCS/Job/ 393)

For An Ai Platform Company.

2 - 4 Years

Full Time

Up to 30 Days

Up to 12 LPA

1 Position(s)

Coimbatore, Chennai

2 - 4 Years

Full Time

Up to 30 Days

Up to 12 LPA

1 Position(s)

Coimbatore, Chennai

Job Description

Jr. Full stack developer

Role Overview: As AI Engineer, you will be at the forefront of shaping the architectural vision for our enterprise AI platform. You’ll work closely with cross-functional teams to create cuttingedge, scalable, and efficient AI solutions that empower businesses with intelligent data insights. Your work will influence product strategy and drive the future of AI in the enterprise space.

Key Responsibilities:

  • • Design, build, and deploy AI agents capable of performing autonomous tasks and interacting with users or systems, enhancing operational efficiency and decision-making.
  • • Implement generative AI solutions and multi-agent systems to drive business innovation, improve automation, and deliver creative problem-solving capabilities across applications.
  • • Work closely with cross-functional teams to define technical requirements, specifications, and ensure smooth integration of AI solutions into existing systems and workflows.
  • • Manage vector databases (e.g., Pinecone, Chroma DB, Milvus) for data ingestion, storage, and optimized query execution, focusing on high performance for similarity search and fast retrieval.
  • • Develop advanced retrieval strategies and prompt engineering techniques to enhance the capabilities of generative models, ensuring efficient and accurate data extraction and model performance

Experience & Skills:

  • • Education Qualification : Bachelor/Master degree in computer science.
  • • Experience: 2-3+ years in software engineering, with 2+ years in AI and 1+ year in generative AI.
  • • Proficient in Python, JavaScript, API Development, AWS/Azure & optimizing database like SQLite and SQL
  • • Experience with LangChain, LangGraph, LlamaIndex, vector databases, and RAG systems for model integration

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