StatusNeo - AI Engineer

4 weeks ago

Bengaluru, India Nexthire Full time

Job Title: AI Engineer
Experience: 9+ Years
Location: Bangalore (Work From Office – 5 Days)
Employment Type: Full-Time

Job Description:

We are looking for a seasoned AI Engineer with over 9 years of experience in designing and deploying AI/ML solutions, with a strong focus on Generative AI (GenAI) and Large Language Models (LLMs) . The ideal candidate should have deep expertise in Python , machine learning , and artificial intelligence , along with a strong understanding of real-world AI productization.

As part of our advanced tech team in Bangalore , you will play a critical role in building and integrating AI-powered applications that solve real business problems.

Key Responsibilities:

  • Design, develop, and deploy AI/ML solutions with a focus on Generative AI and LLMs (e.g., GPT, BERT, Claude, etc.)

  • Fine-tune and customize foundation models for specific domain or business requirements

  • Develop intelligent systems using Python and modern ML frameworks

  • Collaborate with cross-functional teams including product, data engineering, and design to integrate AI into core products

  • Evaluate, test, and implement third-party APIs, libraries, and models relevant to AI/ML/LLM tasks

  • Optimize model performance, latency, and scalability for production use

  • Stay up to date with the latest advancements in AI, machine learning, and GenAI technologies

Required Skills:

  • 9+ years of professional experience in AI/ML roles

  • Strong expertise in Generative AI , LLMs , and natural language processing (NLP)

  • Proficient in Python and libraries like TensorFlow, PyTorch, Hugging Face Transformers

  • Solid background in machine learning algorithms , deep learning, and model evaluation techniques

  • Experience deploying and maintaining ML models in production

  • Strong problem-solving skills and ability to handle ambiguity in requirements

Good to Have:

  • Experience with prompt engineering and few-shot/fine-tuning techniques

  • Knowledge of MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow)

  • Familiarity with vector databases and retrieval-augmented generation (RAG) pipelines

  • Cloud experience (AWS, GCP, or Azure)


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