Lead AI Engineer
at Fulcrum Digital is an agile and next-generation digital acce
Job Description
About the Role
We are seeking an experienced and hands-on Lead AI Engineer with 8-9 years of experience in developing, fine-tuning, and deploying machine learning and deep learning models, including Generative AI systems. The ideal candidate will have strong expertise in classification, anomaly detection, and time-series modeling, along with deep experience in Transformer-based architectures and modern LLM ecosystems.
This role requires technical leadership, architectural decision-making, and mentoring of AI engineers, while actively contributing to building scalable AI solutions. Expertise in model optimization, quantization, and Retrieval-Augmented Generation (RAG) pipelines is highly desirable.
Responsibilities
- Lead the design, development, and deployment of ML and deep learning models for classification, anomaly detection, forecasting, and natural language understanding tasks.
- Architect and build scalable AI and Generative AI solutions, including RAG pipelines for document search, Q&A, summarization, and enterprise knowledge systems.
- Design, train, and fine-tune deep learning models including RNNs, GRUs, LSTMs, and Transformer architectures (e.g., BERT, T5, GPT).
- Drive the fine-tuning and adaptation of large language models (LLMs) using techniques such as Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA.
- Apply model optimization techniques such as quantization, pruning, and efficient inference strategies to improve latency and reduce compute and memory footprint in production systems.
- Define and implement evaluation frameworks, track model performance, monitor model drift, and drive continuous model improvement.
- Lead collaboration with data engineering, backend, platform, and DevOps teams to productionize AI solutions using scalable infrastructure and CI/CD pipelines.
- Provide technical mentorship and guidance to junior and mid-level AI engineers and contribute to best practices in ML engineering.
- Ensure clean, reproducible code, maintain experiment tracking, documentation, and version control of models and datasets.
- Stay up to date with the latest advancements in LLMs, Generative AI, and AI infrastructure, and help drive adoption of new technologies.
Required Skills & Qualifications
- 8-9 years of hands-on experience in machine learning, deep learning, or data science roles.
- Strong programming expertise in Python and ML/DL libraries such as scikit-learn, pandas, PyTorch, and TensorFlow.
- Deep understanding of machine learning algorithms, deep learning architectures, and sequence/NLP modeling techniques.
- Extensive experience with Transformer models and open-source LLM ecosystems (e.g., Hugging Face Transformers).
- Hands-on experience building Generative AI applications and RAG-based systems using frameworks such as LangChain or LlamaIndex.
- Experience with model optimization and quantization techniques (dynamic/static quantization, INT8, etc.) for efficient inference.
- Strong understanding of embeddings, vector databases, and retrieval systems (e.g., FAISS, Pinecone, Azure AI Search).
- Experience with model evaluation, monitoring, and performance optimization in production environments.
- Familiarity with containerization (Docker), experiment tracking (MLflow), and CI/CD pipelines.
- Proven ability to lead technical initiatives and mentor engineering teams.
Preferred Qualifications
- Experience fine-tuning LLMs using SFT, LoRA, or QLoRA on domain-specific datasets.
- Exposure to MLOps platforms such as SageMaker, Vertex AI, or Kubeflow.
- Experience with distributed data processing frameworks like Spark and workflow orchestration tools such as Airflow.
- Contributions to research papers, technical blogs, patents, or open-source projects in ML, NLP, or Generative AI.
- Experience designing enterprise-scale AI platforms or AI-powered products.
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