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Machine Learning Engineer - Data Modeling

Machine Learning Engineer - Data Modeling

Catalyst IQBangalore
30+ days ago
Job description

Key Responsibilities :

LLM & Machine Learning :

  • Work with a variety of LLMs including Hugging Face OSS models, GPT (OpenAI), Gemini (Google), Claude (Anthropic), Mixtral (Mistral), and LLaMA (Meta).
  • Fine-tune and deploy LLMs for various use cases such as summarization, Q&A, RAG (Retrieval Augmented Generation), chatbots, document intelligence, etc.
  • Evaluate and compare model performance and apply optimization & MLOps :
  • Design and implement complete LLMOps workflows using tools like : MLFlow for experiment tracking and model versioning.
  • LangChain, LangGraph, LangFlow for LLM orchestration.
  • Langfuse, LlamaIndex for observability and indexing.
  • AWS SageMaker, Bedrock and Azure AI for model deployment and management.
  • Monitor, log, and optimize inference latency and model behavior in & Vector Stores :
  • Work with structured and unstructured data using MongoDB and PostgreSQL.
  • Leverage vector databases like Pinecone and ChromaDB for RAG-based applications.
  • Develop scalable data ingestion and transformation pipelines for AI training and & DevOps :
  • Deploy and manage AI workloads on AWS and Azure cloud environments.
  • Use Docker and Kubernetes for containerization and orchestration of LLM-based & Integration :
  • Build robust APIs and microservices using Python, with integrations using SQL and JavaScript where needed.
  • Develop UI interfaces or dashboards to visualize model outputs and system Skills :
  • Hands-on experience with multiple LLMs including GPT, Claude, Mixtral, Llama, etc.
  • Expertise in MLOps / LLMOps frameworks : MLFlow, LangChain, LangGraph, LangFlow,

Langfuse, etc.

  • Strong understanding of cloud-native AI deployment (AWS SageMaker, Bedrock, Azure AI).
  • Proficient in vector databases like Pinecone and ChromaDB.
  • Familiarity with DevOps best practices using Docker and Kubernetes.
  • Proficient in Python, SQL, and Qualifications :
  • Previous experience building and deploying production-grade LLM or GenAI applications.
  • Familiarity with real-time or low-latency systems involving LLMs.
  • Certification in AWS or Azure cloud platforms.
  • Exposure to prompt engineering, model fine-tuning, and LLM evaluation techniques

    • (ref : hirist.tech)

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    Machine Learning Engineer • Bangalore