Machine Learning Analyst

Overview

The AI/ML Analyst is responsible for designing, developing, and implementing data-driven AI/Gen AI solutions using machine learning models, LLMs and artificial intelligence techniques. This role bridges applied AI, data science, business strategy, and technical implementation—transforming complex datasets into actionable insights and intelligent systems that support organizational decision-making and automation.

Key Responsibilities

1. ML/AI Model Development

• Build, train, implement and optimize AI solutions adopting machine learning and deep learning and LLM models for business Automation, Insights, prediction, classification, clustering, NLP, or recommendation tasks.
• Agentic AI frameworks and implementation
• Implement model validation, performance tuning, and continuous improvement using best-practice ML pipelines.
• Use frameworks such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar.

2. AI Solutions & Deployment

• Translate business requirements into scalable AI solutions.
• Deploy models into production (APIs, cloud services, Agentic AI, automation pipelines).
• Collaborate with engineering teams to implement MLOps processes.

3. Data Analysis & Feature Engineering

• Collect, clean, and preprocess structured and unstructured data from multiple sources.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and relationships.
• Engineer features that enhance model accuracy and relevance.

4. Reporting & Insights

• Visualize complex data and model outcomes using dashboards (Power BI, Tableau, Looker, etc.).
• Communicate analytical findings to technical and non-technical stakeholders.
• Prepare documentation, reports, and presentations outlining methodologies and results.

5. Research & Innovation

• Stay informed of emerging AI/ML trends, tools, and methodologies.
• Evaluate and implement new technologies such as LLMs, generative AI, vector databases, and reinforcement learning.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or related field.
• Proven experience in GenAI, machine learning, data analytics, and AI model development.
• Strong proficiency in Python, SQL, and ML libraries.
• 2 years of work experience with any hyper scalers like Amazon Web Services (AWS), Azure, Google Cloud
• Experience with cloud platforms (AWS, Azure, GCP) and ML lifecycle tools (MLflow, Kubeflow, SageMaker).
• 2 years of work experience with Machine Tools
• Solid understanding of statistics, probability, and algorithmic concepts.
• 2 years of work experience with Large Language Models (LLM)
• Knowledge of big data technologies (Spark, Databricks, Hadoop).
• Familiarity with deep learning architectures (CNNs, RNNs, Transformers).
• Background in MLOps practices and CI/CD pipelines.
• Industry-specific experience (finance, healthcare, e-commerce and CMT).

Key Competencies

• Analytical and critical-thinking skills.
• Strong problem-solving abilities.
• Excellent communication and data-storytelling skills.
• Ability to work collaboratively in cross-functional teams.
• Attention to detail and a mindset for continuous learning.

Requirements added by the job poster

• Authorized to work in the United States