Arcolab - Senior Consultant - Artificial Intelligence/Machine Learning

Job Description :

We are looking for a highly skilled Senior Consultant Machine Learning / AI with 5-8 years of experience in designing, developing, deploying, and maintaining enterprise-grade Machine Learning solutions. The ideal candidate should have hands-on expertise in the complete ML lifecycle, from data preparation and feature engineering to model development, deployment, monitoring, and continuous improvement.

The role requires strong analytical capabilities, proficiency in Python-based ML frameworks, and the ability to work with both structured and unstructured datasets to solve complex business problems through AI-driven solutions.

Key Responsibilities :

- Design, develop, and implement end-to-end Machine Learning solutions for business use cases.

- Build supervised, unsupervised, and reinforcement learning models based on business requirements.

- Develop predictive analytics models for forecasting, classification, clustering, recommendation, anomaly detection, and optimization.

- Select appropriate machine learning algorithms based on problem statements and business objectives.

- Build reusable and scalable ML pipelines for enterprise applications.

- Perform feature engineering and feature selection to improve model accuracy.

- Optimize model performance through hyperparameter tuning and experimentation.

- Develop explainable AI (XAI) solutions wherever required.

- Collect, clean, preprocess, and transform structured and unstructured datasets.

- Handle missing values, duplicate records, outliers, and inconsistent data.

- Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.

- Engineer meaningful features for improved model performance.

- Work with large datasets using efficient data processing techniques.

- Implement data balancing techniques for imbalanced datasets.

- Validate data quality before model training.

- Train, validate, and evaluate Machine Learning models using appropriate statistical techniques.

- Build classification, regression, clustering, recommendation, and NLP models.

- Implement ensemble learning methods to improve prediction accuracy.

- Perform cross-validation and model comparison.

- Conduct model benchmarking using suitable evaluation metrics.

- Ensure model scalability and robustness.

- Deploy ML models into production environments.

- Develop APIs and prediction services for ML models.

- Integrate ML models with enterprise applications.

- Build automated ML pipelines for continuous model deployment.

- Support CI/CD implementation for Machine Learning workflows.

- Ensure seamless integration with cloud-based AI platforms.

- Continuously monitor deployed Machine Learning models.

- Track model accuracy, drift, latency, and performance metrics.

- Retrain models based on changing business patterns.

- Identify model degradation and recommend improvements.

- Perform root cause analysis for prediction failures.

- Maintain model versioning and lifecycle documentation.

- Ensure high availability and reliability of deployed AI systems.

- Follow Quality Assurance (QA) processes throughout the ML development lifecycle.

- Ensure compliance with enterprise AI governance standards.

- Validate model outputs through rigorous testing methodologies.

- Conduct performance testing and regression testing of AI solutions.

- Maintain documentation for models, datasets, assumptions, and validation results.

- Ensure adherence to coding standards and best development practices.

- Support audit and compliance activities related to AI implementations.

- Develop and maintain Machine Learning development standards.

- Define reusable coding frameworks and AI accelerators.

- Stay updated with emerging AI/ML technologies and industry trends.

- Recommend improvements to existing AI solutions.

- Promote best practices in MLOps, Responsible AI, and model governance.

- Contribute to internal knowledge repositories and technical documentation.

- Work closely with business stakeholders to understand analytical requirements.

- Translate business problems into Machine Learning solutions.

- Collaborate with Data Engineers, Data Scientists, Software Engineers, and DevOps teams.

- Provide technical consulting during solution design and implementation.

- Mentor junior team members and review technical deliverables.

- Participate in architecture discussions and solution planning.

- Support production issue resolution and continuous improvement initiatives.

Required Technical Skills :

1. Programming Languages :

- Python (Mandatory)

- SQL

- R (Good to Have)

2. Machine Learning Libraries :

- Scikit-learn

- TensorFlow

- Keras

- PyTorch

- XGBoost

- LightGBM

- CatBoost

3. Data Processing :

- Pandas

- NumPy

- SciPy

4. Data Visualization :

- Matplotlib

- Seaborn

- Plotly

- Power BI (Preferred)

5. Natural Language Processing (Preferred) :

- NLTK

- SpaCy

- Hugging Face Transformers

- LangChain (Preferred)

6. MLOps :

- MLflow

- Kubeflow

- Azure ML

- AWS SageMaker

Additional Requirements :

- Strong understanding of Machine Learning algorithms.

- Excellent knowledge of statistics and probability.

- Hands-on experience with predictive analytics.

- Expertise in data preprocessing and feature engineering.

- Experience working with structured and unstructured datasets.

- Strong analytical and problem-solving skills.

- Ability to optimize model performance.

- Knowledge of model explainability techniques.

- Understanding of Responsible AI principles.

- Experience with model deployment and monitoring.

- Strong debugging and troubleshooting abilities.

- Excellent communication and stakeholder management skills.

- Ability to mentor junior consultants.

- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related field.

- 5 to 8 years of hands-on experience in Machine Learning and AI solution development.

- Experience working on enterprise AI transformation projects.

- Exposure to Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents is an added advantage.

- Relevant certifications in Azure AI Engineer, AWS Machine Learning, Google Professional Machine Learning Engineer, or TensorFlow are preferred.