Lead AI Engineer

Fulcrum Digital is an agile and next-generation digital acce

Lead AI Engineer

Futran Solutions

Company

Home

Lead AI Engineer

at Fulcrum Digital is an agile and next-generation digital acce

Agency job

via Futran Solutions

8 - 9 yrs

₹35L - ₹38L / yr

Pune

Skills

PyTorch

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.

Job Description

About the Role

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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