AI/ML Engineer

Requirements:

• Bachelors Masters or PhD in a STEM field (Science Technology Engineering or Mathematics).
• Minimum 5 years of experience in machine learning and large-scale data processing.
• Expertise in Python SQL and data processing frameworks such as Pandas NumPy PySpark and Dask is required.
• Experience working with big data tools like Hadoop Apache Spark or similar technologies is essential.
• Proficiency in machine learning frameworks such as TensorFlow PyTorch or similar is expected.
• Hands-on experience in building and deploying large language models (LLMs) and generative AI systems is necessary.
• Strong skills in developing REST APIs and microservices architecture for scalable AI solutions are required.
• Familiarity with version control systems such as Git and implementing CI/CD pipelines for MLOps is important.
• Ability to use data visualization tools such as Tableau Power BI or similar platforms effectively.
• Proven experience in real-time and batch processing of large datasets is needed.
• Skilled in model optimization and monitoring in production environments.
• Experience in applying NLP and generative AI techniques for text analysis and automation is expected.
• Knowledgeable in feature engineering feature selection and time series forecasting.
• Proficiency in database management including both SQL and NoSQL systems is required.
• Proven ability to process analyze and report on large-scale datasets.
• Strong mathematical and statistical foundation.
• Expertise in building data-driven reporting systems and tools.
• Hands-on experience with LLM optimization and generative AI pipelines.
• Knowledge of cloud platforms (AWS Google Cloud or Azure) for scalable data processing.
• Strong communication skills to collaborate with data teams and business stakeholders.
• Understanding of data security and compliance best practices.
• Experience with distributed computing and big data architectures.
• Familiarity with LangChain LlamaIndex Hugging Face and similar LLM-focused tools.
• Knowledge of A/B testing and experimental design for data-driven insights.
• Proficiency in containerization and orchestration (Docker Kubernetes).
• Published research or contributions to AI/ML open-source projects.
• Experience with advanced model optimization and hardware acceleration.

Responsibilities:

• Design and implement end-to-end machine learning solutions to process and analyze large-scale datasets efficiently.
• Build robust data pipelines for high-volume data ingestion transformation and storage.
• Create advanced reporting systems and dashboards powered by AI to deliver actionable insights.
• Develop and deploy custom AI/ML models tailored to business requirements including LLMs and generative AI solutions.
• Leverage frameworks like LangChain LlamaIndex and Hugging Face to implement and fine-tune large language models.
• Build systems to automate data-driven decision-making using predictive analytics and recommendation engines.
• Monitor troubleshoot and optimize AI/ML models in production environments.
• Collaborate with cross-functional teams to enhance reporting and analytics capabilities with AI.
• Ensure scalability reliability and security in handling vast amounts of data.
• Maintain comprehensive documentation for data workflows AI systems and reporting tools.