NC
AI Engineer
NR Consulting - India
- Location not stated
- 14 hours ago
- AI
- Machine Learning
- RAG
- MLOps
- AWS
- Azure
- GCP
- Python
- Core Data
- Pandas
- NumPy
- scikit-learn
- PyTorch
- TensorFlow
- LangChain
- LlamaIndex
- Pinecone
- Milvus
- Chroma
- Hugging Face
- Docker
- REST API
- FastAPI
- Git
- CI/CD
14 hours ago
Location : Mohali
EXP :3 to 5 Years
Description
Job Description:
Key Responsibilities
GenAI & AI Solution Development: Design and deploy modern Generative AI solutions, including Large Language Model (LLM) integrations, Retrieval-Augmented Generation (RAG) pipelines, prompt engineering, and agentic workflows.
Production Systems & MLOps: Build, containerize, and deploy scalable machine learning inference pipelines and APIs into production environments. Monitor, optimize, and troubleshoot model performance, latency, and costs in cloud environments.
Model Development & Experimentation: Apply a strong functional understanding of machine learning and deep learning algorithms. Perform exploratory data analysis, feature engineering, fine-tuning, and rigorous model evaluation.
Cloud Infrastructure (CSP): Leverage cloud services (AWS, Azure, or GCP) to architect robust data and AI workflows, utilizing managed AI services, container registries, and serverless/compute instances.
Cross-Functional Collaboration: Partner closely with software engineering, product, and data teams to seamlessly integrate AI capabilities into customer-facing applications.
Required Qualifications & Skills
Experience: 3–5 years of professional experience in data science, machine learning, or AI engineering, with a proven track record of shipping systems to production.
Programming: Advanced, hands-on proficiency in Python and its core data/ML libraries (e.g., Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow).
Generative AI & Modern AI Tools: Hands-on experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex), vector databases (e.g., Pinecone, Milvus, Chroma), and Hugging Face ecosystems.
Cloud Platforms (CSP): Solid working knowledge of at least one major cloud provider—AWS, Microsoft Azure, or Google Cloud Platform (GCP)—for deploying and managing AI workloads.
Production & Software Engineering Mindset: Familiarity with Docker/containerization, REST API development (FastAPI), Git version control, and CI/CD pipelines.
Core Fundamentals: Strong functional understanding of model development lifecycles, statistical evaluation metrics, data preprocessing, and overcoming common production pitfalls (like data drift or latency bottlenecks).
Preferred Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field.
Experience fine-tuning open-source LLMs or implementing advanced RAG optimizations.
Certifications from AWS, Azure, or GCP in Machine Learning or Cloud Architecture.
AI Engineer · NR Consulting - India