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.