Senior Machine Learning Engineer

4 days ago


Guntur, Andhra Pradesh, India beBeeGenerativeAi Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Job Overview

We are seeking a skilled Machine Learning Engineer to join our team. The ideal candidate will be responsible for designing, developing and deploying state-of-the-art machine learning models with a focus on Generative AI.

This role involves building scalable solutions using Python, large language models (LLMs) and retrieval pipelines to solve complex business problems and enhance product capabilities.

Key Responsibilities:
  • Design, develop and deploy ML solutions using LLMs and RAG architecture.
  • Implement and optimize prompt engineering, embeddings, vector databases and retrieval mechanisms.
  • Develop APIs and services to integrate AI solutions into production systems.
  • Collaborate with data engineers, product managers and other stakeholders to define technical requirements.
  • Continuously evaluate and improve model performance and accuracy.
  • Stay updated with the latest research and developments in GenAI, NLP and ML.
  • Contribute to technical documentation, code reviews and best practices.
  • Ensure all AI solutions comply with ethical and data privacy standards.
Requirements:
  • Bachelor's or Master's degree in Computer Science, AI, Data Science or related field.
  • Strong programming skills in Python including use of libraries like TensorFlow, PyTorch, Hugging Face Transformers, LangChain etc.
  • Experience with LLMs (GPT, LLaMA, Claude etc.) and RAG frameworks.
  • Hands-on experience with vector databases (e.g. FAISS, Pinecone, Weaviate).
  • Solid understanding of NLP, embeddings, tokenization and attention mechanisms.
  • Familiarity with APIs, RESTful services and cloud deployment (AWS/GCP/Azure).
  • Strong foundation in data structures, algorithms and ML workflows.
Preferred Qualifications:
  • Ph.D. in a relevant field.
  • Experience deploying LLMs in production environments.
  • Contributions to open-source GenAI projects or research publications.
  • Knowledge of MLOps and CI/CD for ML pipelines.
  • Experience with Kubernetes, Docker or other containerization tools.
Role Dimensions:
  • Team Size Managed: None / Optional (if leadership role)
  • Scope of Impact: Company-wide AI capabilities and customer-facing features
  • Decision-Making Authority: Technical implementation within AI projects
Success Measures (KPIs):
  • Time to deploy new ML/GenAI features
  • Accuracy and performance of deployed models
  • User engagement and satisfaction with AI features
  • Uptime and reliability of AI services
  • Contribution to AI/ML research or innovation within the team
  • Reusability and scalability of AI code and systems


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