Artificial Intelligence Engineer

We are seeking an innovative and skilled Artificial Intelligence (AI) Engineer to join our team. In this role, you will be responsible for designing, developing, and deploying cutting-edge AI models, machine learning algorithms, and intelligent software applications. You will work closely with cross-functional teams to translate complex business problems into scalable AI-driven solutions that deliver real-world impact.

Key Responsibilities

Model Development & Fine-Tuning: Design, build, train, and evaluate machine learning, deep learning, and generative AI models (LLMs, NLP, computer vision).

AI System Architecture: Architect and integrate AI models into existing production workflows, APIs, and microservices for high availability and low latency.

Data Engineering & Pipeline Integration: Collaborate with data teams to clean, structure, and preprocess large datasets for model training, validation, and feature engineering.

Prompt Engineering & RAG Systems: Implement Retrieval-Augmented Generation (RAG) architectures, vector databases, and advanced prompt engineering strategies for domain-specific AI tasks.

Model Monitoring & Optimization: Monitor model performance in production, implement continuous retraining pipelines, and optimize models for inference speed and resource efficiency.

Research & Innovation: Stay up-to-date with the latest advancements in AI/ML research, frameworks, and open-source models to continuously enhance product capabilities.

Qualifications & Skills

Experience: 3+ years of experience engineering, deploying, and maintaining AI/ML models in production environments.

Programming & Frameworks: Strong proficiency in Python, as well as core AI/ML frameworks like PyTorch, TensorFlow, or Scikit-Learn.

Generative AI & LLMs: Hands-on experience with LLM frameworks (LangChain, LlamaIndex, OpenAI API, Hugging Face) and vector databases (Pinecone, Weaviate, Qdrant).

Software Engineering: Strong understanding of REST APIs, containerization (Docker, Kubernetes), and modern CI/CD software engineering best practices.

Cloud Platforms: Experience with AI deployment services on AWS (SageMaker), GCP (Vertex AI), or Azure AI.

Mathematics & Theory: Solid foundational knowledge in linear algebra, statistics, probability, and optimization algorithms.

Preferred Qualifications

Master’s or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.

Background in developing DevSecOps pipelines for MLOps (ModelOps) tracking using tools like MLflow or Weights & Biases.

Proven track record of taking AI solutions from concept/PoC to full production scale.

What We Offer

Competitive salary and performance bonuses.

Comprehensive medical, dental, and vision health coverage.

401(k) retirement plan with company match.

Flexible work arrangements (Hybrid / Remote).

Paid time off (PTO) and professional development budget for conferences and certifications.