Lead Artificial Intelligence Engineer

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 an experienced and hands-on Lead AI Engineer with 9-10 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

• 9 -10 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.