AI/ML Engineer

About the Role

We are seeking a highly skilled AI/ML Engineer to design, build, and deploy advanced machine learning and Generative AI solutions. This role requires strong hands-on engineering capabilities, deep knowledge of modern AI/ML frameworks, and the ability to translate business requirements into scalable, production-ready systems. You will work closely with cross-functional teams to enable platform adoption, drive automation, and deliver high-impact AI products.

Key Responsibilities

AI/ML Development

• Design and implement supervised and unsupervised models including regression, classification, clustering, time-series forecasting, and boosting methods.
• Build and fine-tune neural networks such as CNNs, RNNs, and LSTMs.

Generative AI

• Develop and integrate solutions powered by LLMs and open-source foundation models.
• Optimize model performance, latency, and cost while ensuring model safety and responsible AI practices.
• Apply prompt engineering, fine-tuning (LoRA, PEFT), domain adaptation, and evaluation techniques.

Modern Software Engineering

• Write efficient, scalable, and maintainable Python code using tools like JupyterLab and VSCode.
• Package and deploy solutions using Docker and Kubernetes on cloud platforms including AWS and Azure.
• Use Git for version control and enforce SWE best practices.

Model Management & Deployment

• Manage full model lifecycle through robust MLOps practices.
• Serialize models using Pickle, Joblib, or ONNX.
• Deploy models using FastAPI or serverless frameworks, ensuring secure and scalable API endpoints.
• Build internal AI applications using Streamlit or front-end technologies (HTML/CSS/JavaScript).

Platform Enablement

• Use Databricks to accelerate AI/ML development, automate model workflows, and promote platform adoption.
• Support AutoML, feature engineering pipelines, and reusable templates.

Hands-On Expertise

• Perform advanced data processing, exploration, visualization, and storytelling.
• Work with libraries such as scikit-learn, PyTorch, pandas, polars, NumPy, seaborn, and others for data prep, model building, and insights generation.

Systems Thinking

• Approach problems with an end-to-end mindset data quality, model performance, infrastructure, UX, and downstream application impacts.
• Translate business objectives into scalable and reliable technical solutions.

Collaboration & Mentorship

• Partner with stakeholders, data teams, product teams, and engineering groups to deliver business value.
• Mentor junior engineers and data scientists on data quality, governance, solution accessibility, and AI/ML best practices.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field (preferred).
• 3-8+ years of experience in AI/ML engineering or data science roles.
• Proven experience deploying production-grade AI/ML systems on cloud platforms.
• Strong communication and problem-solving skills