AI Engineer

About the Role

We are looking for an energetic, hands-on AI Engineer / Data Scientist with 4 to 6 years of

experience to join our core AI and engineering team. In this role, you will design, build, and

scale production-grade machine learning and Generative AI systems.

You will work at the intersection of data science and software engineering, taking models from

conception and functional development to scalable cloud deployment on AWS/Azure/GCP. If

you are passionate about building high-performance AI solutions, working with LLMs/GenAI

tools, and writing clean, production-ready Python code, we want to hear from you.

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: 36 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.