Sr AI Engineers

Responsibilities

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  Design, train, and evaluate ML models for classification, anomaly detection, forecasting, and natural language understanding tasks.

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  Build and fine-tune deep learning models, including RNNs, GRUs, LSTMs, and Transformer architectures (e.g., BERT, T5, GPT).

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  Develop and deploy Generative AI solutions, including RAG pipelines for applications such as document search, Q&A, and summarization.

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  Apply model optimization techniques, including quantization, to improve latency and reduce memory/compute overhead in production.

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  Fine-tune large language models (LLMs) using Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA (optional).

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  Define, track, and report relevant evaluation metrics; monitor model drift and retrain models as required.

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  Collaborate with cross-functional teams (data engineering, backend, DevOps) to productionize ML models using CI/CD pipelines.

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  Maintain clean, reproducible code, and proper documentation and versioning of experiments.

Required Skills & Qualifications

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  4–5 years of hands-on experience in machine learning, deep learning, or data science roles.

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  Proficiency in Python and ML/DL libraries: scikit-learn, pandas, PyTorch, TensorFlow.

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  Strong understanding of traditional ML and deep learning, particularly for sequence and NLP tasks.

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  Experience with Transformer models and open-source LLMs (e.g., Hugging Face Transformers).

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  Familiarity with Generative AI tools and RAG frameworks (e.g., LangChain, LlamaIndex).

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  Experience in model quantization (dynamic/static, INT8) and deploying models in resource-constrained environments.

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  Knowledge of vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques.

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  Proficiency in evaluating models using statistical and business metrics.

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  Experience with model deployment, monitoring, and performance tuning in production.

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  Familiarity with Docker, MLflow, and CI/CD practices.

Preferred Qualifications

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  Experience fine-tuning LLMs (SFT, LoRA, QLoRA) on domain-specific datasets.

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  Exposure to MLOps platforms (e.g., SageMaker, Vertex AI, Kubeflow).

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  Familiarity with distributed data processing frameworks (e.g., Spark) and orchestration tools (e.g., Airflow).

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  Contributions to research papers, blogs, or open-source projects in ML/NLP/Generative AI.