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SR AI Engineer
SR AI EngineerFulcrum Digital Inc • Kanpur, IN
SR AI Engineer

SR AI Engineer

Fulcrum Digital Inc • Kanpur, IN
2 hours ago
Job description

We are seeking a skilled and hands-on Sr. AI Engineer with 4–8 years of experience in developing, fine-tuning, and deploying machine learning and deep learning models, including Generative AI systems. The ideal candidate has a strong foundation in classification, anomaly detection, and time-series modeling, along with experience in Transformer-based architectures. Expertise in model optimization, quantization, and Retrieval-Augmented Generation (RAG) pipelines is highly desirable.

Exp-4-8 Years

Notice Period-Immediate-15 Days

Location-Pune(Hybrid)

Responsibilities

  • Design, train, and evaluate ML models for classification, anomaly detection, forecasting, and natural language understanding tasks.
  • Build and fine-tune deep learning models, including RNNs, GRUs, LSTMs, and Transformer architectures (e.g., BERT, T5, GPT).
  • Develop and deploy Generative AI solutions, including RAG pipelines for applications such as document search, Q&A, and summarization.
  • Apply model optimization techniques, including quantization, to improve latency and reduce memory / compute overhead in production.
  • Fine-tune large language models (LLMs) using Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA (optional).
  • Define, track, and report relevant evaluation metrics; monitor model drift and retrain models as required.
  • Collaborate with cross-functional teams (data engineering, backend, DevOps) to productionize ML models using CI / CD pipelines.
  • Maintain clean, reproducible code, and proper documentation and versioning of experiments.

Required Skills & Qualifications

  • 4–5 years of hands-on experience in machine learning, deep learning, or data science roles.
  • Proficiency in Python and ML / DL libraries : scikit-learn, pandas, PyTorch, TensorFlow.
  • Strong understanding of traditional ML and deep learning, particularly for sequence and NLP tasks.
  • Experience with Transformer models and open-source LLMs (e.g., Hugging Face Transformers).
  • Familiarity with Generative AI tools and RAG frameworks (e.g., LangChain, LlamaIndex).
  • Experience in model quantization (dynamic / static, INT8) and deploying models in resource-constrained environments.
  • Knowledge of vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques.
  • Proficiency in evaluating models using statistical and business metrics.
  • Experience with model deployment, monitoring, and performance tuning in production.
  • Familiarity with Docker, MLflow, and CI / CD practices.
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