Generative AI, Machine Learning Engineer

Job Summary 

We are seeking an experienced Generative AI, Machine Learning & Data Engineer to design, build, and deploy advanced AI/ML solutions and scalable data infrastructure. The ideal candidate will work across the ML lifecycle from data engineering and feature pipelines to training, fine-tuning, deployment, and monitoring of generative AI and machine learning models. This role collaborates with cross-functional teams to integrate AI/ML capabilities into production systems and drive business outcomes.

Key Responsibilities

1. Design and Develop AI/ML Solutions: Architect, build, and optimize machine learning and generative AI models (e.g., transformer-based LLMs, NLP systems, synthetic data pipelines) for production applications.
2. End-to-End Data Engineering: Create and maintain robust, scalable data pipelines using tools like PySpark, SQL, Python, or similar frameworks for large-scale data processing and feature engineering.
3. MLOps and Model Deployment: Implement CI/CD and MLOps practices for machine learning workflows, including model versioning, automated training, monitoring, and rollback strategies.
4. Model Fine-Tuning & Evaluation: Fine-tune language and generative models (e.g., GPT, BERT, diffusion models) and evaluate performance against business-specific metrics.
5. Collaborate Across Teams: Work with data scientists, software engineers, product owners, and business stakeholders to translate requirements into scalable AI/ML solutions.
6. Documentation & Best Practices: Produce high-quality documentation, adhere to engineering standards, and share knowledge with team members.
7. Stay Current with Research: Keep up to date with emerging AI/ML and generative AI advancements to continually improve model quality and engineering approaches.

Required Skills & Qualifications

• Education: Master’s degree in Computer Science, Data Science, Engineering, or a related technical field (advanced degrees preferred).
• Core Programming: Strong proficiency in Python (or similar languages) with experience in data processing and ML libraries — e.g., Pandas, NumPy, PyTorch, TensorFlow.
• Machine Learning & AI: Proven experience applying machine learning techniques, including LLMs, transformer architectures, and generative AI models.
• Cloud & MLOps: Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps technologies like MLflow, Kubeflow, Docker, Kubernetes, and CI/CD tooling.
• Model Deployment: Experience deploying and serving ML/GenAI models via APIs or microservices in production environments.
• Analytical Skills: Strong analytical, problem-solving, and communication skills to translate complex technical concepts into actionable solutions.

Preferred / Nice-to-Have

• Specialized GenAI Experience: Hands-on work with RAG (Retrieval-Augmented Generation), prompt engineering, and vector databases (FAISS, Milvus, etc.).
• Advanced Analytics: Experience with feature stores, real-time data processing frameworks, or streaming platforms (Kafka, Flink).