AI Engineer

2 days ago

Hyderabad, Telangana, India Aptagrim Consulting Full time ₹ 8,00,000 - ₹ 24,00,000 per year

We are seeking a highly skilled and hands-on AI Engineer with experience in building and deploying deep learning models. If youre passionate about large language models (LLMs), generative AI, and developing intelligent systems in computer vision and natural language processing (NLP), we'd love to meet you.

You will be at the forefront of creating production-ready AI solutions, working closely with cross-functional teams to design, train, optimize, and deploy state-of-the-art models that solve real-world problems.

Key Responsibilities

  • Design, train, and fine-tune models for tasks such as:
  • Image classification, segmentation, object detection
  • Text classification, summarization, generation
  • Build and integrate generative AI solutions using LLMs (e.g., GPT, LLaMA, BERT)
  • Optimize and deploy AI models in scalable, GPU-accelerated environments
  • Collaborate with engineering, DevOps, and IT teams to ensure robust model deployment and monitoring
  • Translate business requirements into AI solutions through close interaction with product and customer teams
  • Continuously improve model performance with respect to accuracy, latency, and resource efficiency

Requirements

  • Strong understanding of deep learning, computer vision, and NLP fundamentals
  • Hands-on experience with:
  • CNNs, RNNs/LSTMs, attention mechanisms, transformers
  • Generative models such as diffusion models, text-to-image models, or LLMs
  • Proficiency in PyTorch or TensorFlow
  • Experience working with LLM platforms such as Hugging Face, OpenAI, or similar
  • Exposure to prompt engineering, fine-tuning, or embedding-based retrieval
  • Familiarity with GPU-based training workflows, distributed computing, or MLOps tools is a plus
  • Solid programming skills in Python and comfort with Git, Docker, or related tools

Good to Have

  • Experience with vector databases (e.g., FAISS, Pinecone)
  • Knowledge of ML lifecycle tools like MLflow, Weights & Biases, or DVC
  • Exposure to cloud platforms (AWS, GCP, or Azure) for model training/deployment

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