Artificial Intelligence Engineer

We are seeking a skilled and hands-on AI Engineer with 45 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-4-6 Years

NP-Immediate-10 Days

Location- Pune

Mode-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

• 45 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).

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• 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.

Preferred Qualifications

• Experience fine-tuning LLMs (SFT, LoRA, QLoRA) on domain-specific datasets.
• Exposure to MLOps platforms (e.g., SageMaker, Vertex AI, Kubeflow).
• Familiarity with distributed data processing frameworks (e.g., Spark) and orchestration tools (e.g., Airflow).
• Contributions to research papers, blogs, or open-source projects in ML/NLP/Generative AI.