Software Engineer – AI/ML, LLMs

1 day ago

Bengaluru, Karnataka, India NIBODHAH Full time

Key Responsibilities

  • Design, develop, and deploy RAG (Retrieval-Augmented Generation) systems integrating vector databases, embedding models, and custom retrievers.
  • Architect and fine-tune custom LLMs (via parameter-efficient tuning, LoRA, or domain-specific training).
  • Create and manage agentic AI workflows—multi-step reasoning systems powered by tools such as LangChain, CrewAI, or OpenDevin.
  • Implement prompt engineering and prompt-chaining techniques to optimize LLM behavior and reliability.
  • Build and maintain scalable LangChain-based applications that interact with databases, APIs, and business systems.
  • Collaborate with data scientists to design and deploy ML/AI pipelines leveraging AWS, Azure, or GCP cloud infrastructure.
  • Develop, test, and optimize APIs that integrate AI reasoning and autonomous decision-making capabilities.
  • Monitor and improve model performance, latency, and token efficiency using observability tools like LangFuse, Phoenix, or OpenDevin dashboards.
  • Document architectural decisions, experiment metrics, and system flows for technical transparency.
  • ‍

Required Skills and Experience

  • 4+ years in Python software development, with strong understanding of asynchronous programming and API design.
  • Solid experience deploying LLM applications using frameworks like LangChain, LlamaIndex, or Haystack.
  • Proficiency working with vector databases (FAISS, Pinecone, Weaviate, Milvus, Qdrant) and embedding models (OpenAI, sentence-transformers, Cohere, HuggingFace).
  • Experience designing autonomous agent systems, connecting LLMs with Python functions, tools, or APIs.
  • Strong command of prompt engineering, including contextual grounding, role prompting, and few-shot examples.
  • Familiarity with RAG pipeline architecture (retriever, ranker, generator layering).
  • Understanding of custom model fine-tuning techniques (RLHF, LoRA, QLoRA, PEFT).
  • Experience with cloud-based model deployment (AWS Sagemaker, Azure ML, Vertex AI).
  • Working knowledge of AI observability, evaluation frameworks (LangFuse, Traceloop, Phoenix).
  • ‍

Preferred Qualifications

  • Experience building autonomous agents using frameworks like LangGraph, CrewAI, OpenDevin, or AutoGPT.
  • Experience deploying multi-agent collaboration systems or tool-using AI.
  • Background in knowledge graph integration, context window optimization, and embedding search tuning.
  • Contribution to open-source AI/LLM projects.
  • Strong understanding of data governance, AI ethics, and model interpretability.
  • ‍

Technical Environment

Languages:
Python

Frameworks:
LangChain, LlamaIndex, Django, FastAPI

Databases:
 PostgreSQL, MySQL, and vector DBs (Pinecone, FAISS, Qdrant, Weaviate)

Cloud:
AWS (S3, Lambda, RDS, SageMaker), Azure, or GCP

AI Stack:
 OpenAI, Anthropic Claude, Meta Llama, Mistral, HuggingFace Hub

Architecture:
 RAG, multi-agent orchestration, microservices, serverless.

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