Artificial Intelligence/Machine Learning Engineer - NLP/LLM

Description :

We are looking for a versatile AI/ML powerhouse to join our core engineering team. Whether you are an expert at building high-performance models or a visionary architect who designs entire ML ecosystems, your mission is to transform raw data into intelligent, scalable products. You will work at the intersection of data science and software engineering to solve complex business problems using state-of-the-art machine learning techniques.

Key Responsibilities (Tailored by Seniority) :

- [The Engineer Level] : Design, develop, and train machine learning models (Supervised/Unsupervised) to solve specific business use cases such as predictive analytics, recommendation engines, or automation.

- [The Senior Level] : Lead the research and implementation of advanced architectures (e.g., Deep Learning, NLP, or Generative AI/LLMs). Optimize models for production-grade performance and mentor junior team

members.

- [The Architect Level] : Define the end-to-end AI strategy. Design the MLOps lifecycle, including data pipelines, model versioning, deployment strategies (A/B testing, Shadow deployments), and long-term scalability of the AI platform.

- Data Engineering & Feature Extraction : Collaborate with data engineers to build robust pipelines using Spark, Flink, or Kafka to ensure high-quality data for model training.

- Performance Tuning : Monitor and refine models in production to prevent "model drift" and ensure continuous accuracy and reliability.

Qualifications & Experience :

- Educational Background : BS/MS/PhD in Computer Science, Mathematics, Statistics, or a related quantitative field.

Industry Experience :

- Engineer : 4+ years of hands-on experience in building and deploying ML models.

- Senior : 7+ years with a track record of leading complex ML projects from research to production.

- Architect : 10+ years with experience designing distributed systems and enterprise-level AI infrastructures.

- Problem-Solving : A strong mathematical foundation in linear algebra, calculus, and probability.

- Communication : Ability to explain complex technical concepts to non-technical business stakeholders.

(ref:hirist.tech)