StatusNeo - AI Engineer
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)
