Senior AI/ML/GenAI Engineer_Remote_WFH

Required Skills & Experience

Experience Level

• 5-8 years of professional experience in Data Science , ML / DL Architecturing , Generative AI engineering or AI/ML development roles
• Proven track record of deploying and maintaining production ML systems at scale
• Experience in digital marketing, customer service, contact centers, or related industries strongly preferred

Technical Expertise

Programming & Core Libraries

Python Ecosystem Mastery (Required)

• Advanced Python 3.9+ proficiency with deep understanding of object-oriented programming, decorators, context managers, and asyncio for concurrent processing
• Data Manipulation & Analysis:
• Pandas 2.0+: Advanced operations including multi-indexing, groupby operations, window functions, categorical data handling, and performance optimization techniques
• NumPy 1.24+: Vectorized operations, broadcasting, advanced indexing, custom ufuncs, and memory-efficient array operations
• Polars: Next-generation DataFrame library for faster data processing and memory efficiency
• Big Data Processing:
• PySpark 3.4+: DataFrame API, Spark SQL, MLlib, structured streaming, and cluster optimization
• Dask: Parallel computing, delayed operations, distributed arrays, and scaling pandas workflows
• Scientific Computing:
• SciPy: Statistical functions, optimization algorithms, signal processing, and numerical integration
• Statsmodels: Advanced statistical modeling, time series analysis (ARIMA, GARCH, VAR), and econometric analysis
• Scikit-learn 1.3+: Custom transformers, pipeline optimization, advanced cross-validation strategies, and model interpretation tools

Machine Learning Frameworks (Expert Level)

• Deep Learning Platforms:
• PyTorch 2.0+: Dynamic computational graphs, autograd, custom loss functions, distributed training, and TorchScript optimization
• TensorFlow 2.13+: Eager execution, tf.function optimization, custom layers, TensorBoard integration, and TensorFlow Serving
• Keras 3.0: High-level API mastery, custom metrics/callbacks, transfer learning, and model subclassing
• Gradient Boosting Libraries:
• XGBoost 2.0+: Advanced hyperparameter tuning, custom objective functions, early stopping, and SHAP integration
• LightGBM 4.0+: Categorical feature handling, dart boosting, and GPU acceleration
• CatBoost: Handling high-cardinality categorical features and automatic feature engineering
• Specialized ML Libraries:
• Optuna/Hyperopt: Bayesian optimization for hyperparameter tuning
• SHAP/LIME: Model interpretability and explainable AI
• Imbalanced-learn: Handling class imbalance with SMOTE, ADASYN, and ensemble methods

Generative AI & LLM Technologies

Large Language Model Platforms (Production Experience Required)

• OpenAI Ecosystem:

• GPT-4/GPT-4 Turbo: Advanced prompting strategies, function calling, vision capabilities, and fine-tuning with GPT-3.5
• OpenAI API: Rate limiting, error handling, cost optimization, and embedding API usage
• Whisper: Speech-to-text integration for call center voice analysis
• DALL-E: Image generation for automated content creation

• Google AI Platform:

• Gemini Pro/Ultra: Multimodal capabilities, context length optimization, and enterprise integration
• Vertex AI Model Garden: Deployment and management of foundation models
• Meta

  AI Models:

• Llama 2/3: Open-source deployment, custom fine-tuning, and performance optimization
• Code Llama: Automated code generation and review processes
• Anthropic Claude: Constitutional AI principles, safety considerations, and enterprise deployment
• Open Source Alternatives: Mistral AI, Falcon, MPT, and Alpaca models for cost-effective solutions

GenAI Development Frameworks & Tools

• LangChain 0.1+:

• Advanced chain composition, memory management, and custom tool creation
• Agent frameworks for autonomous task execution
• Integration with external APIs and databases
• Streaming responses and async processing

• LangGraph

  :
• Stateful multi-agent workflow orchestration and complex reasoning chains
• Cyclic graph construction for iterative problem-solving and self-correction loops
• State management for long-running conversational agents
• Human-in-the-loop integration for escalation and approval workflows
• Parallel execution and conditional branching for complex decision trees
• Graph visualization and debugging for workflow optimization

• LangSmith

  :
• Production LLM monitoring, logging, and performance analytics
• A/B testing frameworks for prompt optimization and model comparison
• Dataset creation and management for fine-tuning and evaluation
• Automated evaluation pipelines with custom metrics and human feedback integration
• Error tracking and debugging for complex multi-step LLM workflows
• Cost monitoring and optimization across different LLM providers
• Annotation tools for creating high-quality training datasets
• Production deployment monitoring with alerting and anomaly detection

Vector Databases & Semantic Search

• Weaviate: GraphQL APIs, custom vectorization, and multi-modal search

Advanced LLM Techniques

• Fine-tuning Methodologies:

• LoRA (Low-Rank Adaptation): Memory-efficient fine-tuning for domain adaptation
• QLoRA: Quantized LoRA for reduced memory footprint
• Instruction Tuning: Creating custom instruction datasets and training procedures
• RLHF (Reinforcement Learning from Human Feedback): Implementing reward models and PPO training

• Prompt Engineering:

• Advanced prompting techniques: Chain-of-thought, few-shot learning, and constitutional AI
• Prompt optimization: Systematic prompt testing and A/B testing frameworks
• Dynamic prompting: Context-aware prompt generation and adaptive prompting strategies

• RAG (Retrieval-Augmented Generation):

• Advanced RAG architectures: Multi-hop reasoning, query decomposition, and re-ranking strategies
• Hybrid search: Combining dense and sparse retrieval methods
• Context optimization: Document chunking strategies, overlap management, and context compression

• Model Optimization:

• Quantization: INT8/INT4 quantization for inference optimization
• Distillation: Knowledge distillation from large to smaller models
• Pruning: Structured and unstructured pruning techniques

Data Science Methodologies & Statistical Analysis

Advanced Statistical Methods

• Hypothesis Testing & Experimental Design:

• A/B Testing: Power analysis, sample size calculation, multiple testing correction (Bonferroni, FDR)
• Bayesian Statistics: Prior specification, MCMC sampling, hierarchical modeling with PyMC or Stan
• Causal Inference: Difference-in-differences, instrumental variables, propensity score matching, and causal DAGs
• Survival Analysis: Cox regression, Kaplan-Meier estimation, and time-to-event modeling

• Multivariate Analysis:

• Dimensionality Reduction: PCA, t-SNE, UMAP, Factor Analysis, and manifold learning techniques
• Clustering: K-means++, DBSCAN, hierarchical clustering, and mixture models
• Classification & Regression: Logistic regression, SVM, ensemble methods, and neural networks

• Time Series Analysis:

• Classical Methods: ARIMA, SARIMA, Exponential Smoothing, and seasonal decomposition
• Modern Techniques: Prophet, Neural Prophet, and Transformer-based forecasting
• Multivariate Time Series: VAR models, Granger causality, and state-space models

Feature Engineering & Data Preprocessing

• Advanced Feature Engineering:

• Automated Feature Engineering: Featuretools, AutoFeat, and genetic programming approaches
• Feature Selection: Recursive feature elimination, mutual information, and stability selection
• Categorical Encoding: Target encoding, entity embeddings, and hash encoding
• Text Feature Engineering: TF-IDF variants, word embeddings, and n-gram analysis

• Data Quality & Validation:

• Data Profiling: Automated data quality assessment and anomaly detection
• Missing Data Handling: Multiple imputation, matrix completion, and missingness pattern analysis
• Outlier Detection: Isolation Forest, Local Outlier Factor, and robust statistical methods
• Data Drift Detection: Population stability index, KL divergence, and distribution monitoring

Model Development & Validation

• Cross-Validation Strategies:

• Time Series CV: Walk-forward validation, blocked cross-validation, and gap-based splitting
• Stratified CV: Maintaining class balance and group-based splitting
• Nested CV: Unbiased hyperparameter optimization and model selection

• Model Interpretability:

• Global Interpretation: Feature importance, partial dependence plots, and SHAP summary plots
• Local Interpretation: LIME, SHAP values, and counterfactual explanations
• Model-Agnostic Methods: Permutation importance and accumulated local effects

• Performance Evaluation:

• Classification Metrics: ROC-AUC, PR-AUC, F1-score variants, and calibration assessment
• Regression Metrics: MAPE, SMAPE, directional accuracy, and residual analysis
• Business Metrics: Customer lifetime value, churn prediction accuracy, and revenue impact assessment
  

Advanced Statistical Modeling & Machine Learning

Contact Center Analytics & Voice Intelligence

• Speech Analytics & NLP:

• Conversation Intelligence: Automatic topic detection, intent classification, and sentiment progression
• Voice Emotion Recognition: Acoustic feature extraction and emotion classification models
• Speaker Analytics: Voice biometrics, accent detection, and speech quality assessment

• Workforce Analytics:

• Agent Scheduling Optimization: Integer programming for shift scheduling with constraints
• Performance Prediction: Predicting agent success during hiring and ongoing performance
• Training Effectiveness: Measuring training ROI and skill development tracking

• Customer Journey Analytics:

• Omnichannel Attribution: Cross-channel customer journey mapping and touchpoint analysis
• Intent Prediction: Predicting customer intent from early interaction signals
• Effort Prediction: Customer effort score prediction and friction point identification

Required Education

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Mathematics, or related technical field
• Advanced degree preferred for senior-level positions

Industry Experience

Domain Expertise (Preferred)

• Call center and customer service industry experience with understanding of contact center operations
• Telecommunications or SaaS industry background
• Customer experience (CX) technology and analytics
• Real-time systems development for customer-facing applications
• Regulatory compliance experience in data-sensitive industries