Lead AI ML Engineer

Role & responsibilities

Mandatory skills: Python, Gen AI, traditional ML, core Data scientist

Position Overview

• Design, develop, and deploy machine learning models ranging from classical algorithms to deep learning for production environments
• Apply traditional statistical methods including hypothesis testing, regression analysis, time series forecasting, and experimental design
• Build and optimize large language model applications including fine-tuning, prompt engineering, and model evaluation
• Implement Retrieval Augmented Generation (RAG) systems for enhanced AI capabilities
• Conduct advanced data analysis, statistical modeling, A/B testing, and predictive analytics using both classical and modern techniques
• Research and prototype cutting-edge generative AI solutions

Traditional ML & Statistics:

• Implement classical machine learning algorithms including linear/logistic regression, decision trees, random forests, SVM, clustering, and ensemble methods
• Perform feature engineering, selection, and dimensionality reduction techniques
• Conduct statistical inference, confidence intervals, and significance testing
• Design and analyze controlled experiments and observational studies
• Apply Bayesian methods and probabilistic modeling approaches

Full Stack Development:

• Develop scalable front-end applications using modern frameworks (React, Vue.js, Angular)
• Build robust backend services and APIs using Python, Node.js, or similar technologies
• Design and implement database solutions (SQL/NoSQL) optimized for ML workloads
• Create intuitive user interfaces for AI-powered applications and statistical dashboards

MLOps & Infrastructure:

• Establish and maintain ML pipelines for model training, validation, and deployment
• Implement CI/CD workflows for ML models using tools like MLflow, Kubeflow, or similar
• Monitor model performance, drift detection, and automated retraining systems
• Deploy and scale ML solutions using cloud platforms (AWS, GCP, Azure)
• Containerize applications using Docker and orchestrate with Kubernetes

Collaboration & Leadership:

• Work closely with data scientists, product managers, and engineering teams
• Mentor junior engineers and contribute to technical decision-making
• Participate in code reviews and maintain high development standards
• Stay current with latest AI/ML trends and technologies

Required Qualifications

• 7-8 years of professional software development experience
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Machine Learning, Data Science, or related field
• 6+ years of hands-on AI/ML experience in production environments

Technical Skills:

• Programming:

  Expert proficiency in Python, strong experience with JavaScript/TypeScript, R is a plus

• Traditional ML:

  Scikit-learn, XGBoost, LightGBM, classical algorithms and ensemble methods

• Statistics:

  Hypothesis testing, regression analysis, ANOVA, time series analysis, experimental design, Bayesian inference

• Statistical Tools:

  Experience with R, SAS, SPSS, or similar statistical software packages

• Deep Learning:

  TensorFlow, PyTorch, neural networks, computer vision, NLP

• LLM Experience:

  Working with GPT, Claude, Llama, or similar models; experience with fine-tuning and prompt engineering

• RAG Implementation:

  Vector databases (Pinecone, Weaviate, Chroma), embedding models, semantic search

• Data Science:

  Pandas, NumPy, statistical analysis, data visualization (Matplotlib, Plotly, Seaborn), feature engineering

• Full Stack:

  React/Vue.js, Node.js/FastAPI, REST/GraphQL APIs

• Databases:

  PostgreSQL, MongoDB, Redis, vector databases

• MLOps:

  Docker, Kubernetes, CI/CD, model versioning, monitoring tools

• Cloud Platforms:

  AWS/GCP/Azure, serverless architectures

Soft Skills:

• Strong problem-solving and analytical thinking
• Excellent communication and collaboration abilities
• Self-motivated with ability to work in fast-paced environments
• Experience with agile development methodologies

Preferred Qualifications

• Experience with causal inference methods and econometric techniques
• Knowledge of distributed computing frameworks (Spark, Dask)
• Experience with edge AI and model optimization techniques
• Publications in AI/ML/Statistics conferences or journals
• Open source contributions to ML/statistical projects
• Experience with advanced statistical modeling and multivariate analysis
• Familiarity with operations research and optimization techniques