Key skills
PythonAIMachine LearningMLDeep LearningNLPGenerative AILarge Language ModelsRAGLangChainLlamaIndexPyTorchTensorFlowscikit-learnPandasHugging FaceMLOpsMLflowKubeflowPineconeData EngineeringSparkAirflowAzureDockerSageMakerVertex AIAgileCI/CD
About this role
Who are we
Fulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services right from ideation to implementation. These services have applicability across a variety of industries, including banking & financial services, insurance, retail, higher education, food, healthcare, and manufacturing.
About the Role We are seeking a skilled and hands-on AI Engineer with 4–5 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.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.