Sr. AI Engineer

Fulcrum Digital; Pune District; On-site; INR 400,000 - 700,000; Full time

Job summary

Fulcrum Digital is seeking a highly skilled Senior AI Engineer to lead the design, development, and production deployment of advanced AI solutions including traditional ML, DL, and Generative AI. You will own end-to-end AI initiatives from architecture to monitoring, building scalable enterprise applications such as conversational AI assistants and RAG pipelines that deliver measurable business value.

You will design, train and evaluate models, architect GenAI/RAG solutions, and deploy on cloud

Qualifications

• 5+ years of hands-on ML/AI engineering with production deployment experience.

Responsibilities

• Design, train, and evaluate ML and DL models for NLP, forecasting, and anomaly detection.
• Architect and develop Generative AI and RAG solutions for document search and conversational QA.
• Implement vector stores and embeddings for grounded, context-aware responses.
• Fine-tune LLMs with SFT/PEFT (LoRA/QLoRA) on domain data where needed.
• Optimize models with quantization to reduce latency and cost.
• Deploy production-grade models on AWS/Azure/GCP with CI/CD and Docker/Kubernetes.
• Collaborate with data eng, backend, DevOps, and product teams; write clean, reproducible code.

Skills

Python

Java

C++

Deep Learning

NLP

Cloud Platforms

Docker

Kubernetes

TensorFlow

PyTorch

LangChain

LlamaIndex

Hugging Face

Education

Bachelor's or Master's in Computer Science/Data Science

Tools

TensorFlow

PyTorch

Docker

Kubernetes

scikit-learn

pandas

Hugging Face

Job description

Fulcrum Digital is an agile and next-generation digital accelerating company providing digital transformation and technology services from ideation to implementation across industries including banking & financial services, insurance, retail, higher education, food, healthcare, and manufacturing.

Position Overview

We are seeking a highly skilled, hands-on Senior AI Engineerto lead the design, development, and production deployment of advanced AI solutions - spanning traditional machine learning, deep learning, and Generative AI. You will own end-to-end AI initiatives, from architecture through optimization, deployment, and monitoring, and build scalable, enterprise-grade applications such as conversational AI assistants and Retrieval-Augmented Generation (RAG) pipelines that deliver measurable business value.

Key Responsibilities

• Design, train, and evaluate ML and deep learning models (RNNs, GRUs, LSTMs, and Transformers such as BERT, T5, GPT) for classification, anomaly detection, forecasting, and NLP tasks.
• Architect and develop Generative AI and RAG solutions for document search, conversational Q&A, and summarization using frameworks like LangChain and LlamaIndex.
• Implement vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques for grounded, context-aware responses.
• Optionally fine-tune LLMs using SFT and PEFT methods (LoRA, QLoRA) on domain-specific datasets.
• Optimize models via quantization (dynamic/static, INT8) to improve latency and reduce compute overhead.
• Deploy models into production on cloud platforms (AWS, Azure, GCP) using containerization (Docker, Kubernetes), collaborating with DevOps on CI/CD pipelines for scalability and reliability.
• Define and track technical and business metrics; monitor model drift and performance, and retrain as needed.
• Collaborate with cross-functional teams (data engineering, backend, DevOps, product) and mentor junior engineers; write clean, reproducible, well-documented code.

Required Qualifications

• Bachelor's or Master's in Computer Science, Data Science, or a related field.
• 5+ yearsof hands-on experience in machine learning, AI engineering, or data science, with proven production deployment experience.
• Proficiency in programming languages such as Python, Java, or C++.
• Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) and traditional machine learning algorithms, especially for sequence and NLP tasks.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Proficiency with ML/DL libraries (scikit-learn, pandas) and Transformer models / open-source LLMs (e.g., Hugging Face).
• Practical experience with GenAI tools, RAG frameworks, vector stores, and embeddings.
• Experience with model quantization, evaluation using statistical and business metrics, and production monitoring.
• Familiarity with MLflow and CI/CD practices.
• Excellent problem-solving and communication skills; able to work independently and collaboratively.

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 (e.g., Spark, Hadoop) and orchestration tools (e.g., Airflow).
• Experience building enterprise-grade conversational or agentic AI solutions.
• Familiarity with computer vision and version control (Git).
• Contributions to research papers, blog posts, or open-source projects in ML/NLP/GenAI.