AI/ML Expert

Data Science & Advanced Analytics

Analyze large and complex datasets to uncover trends, patterns, anomalies, and actionable business insights.
Perform exploratory data analysis (EDA), data profiling, and data quality assessments.
Apply statistical techniques and predictive modeling to solve business challenges.
Develop forecasting, classification, clustering, recommendation, and optimization models.
Translate business problems into analytical approaches and provide data-driven recommendations.
Design and measure experiments, evaluate outcomes, and communicate findings to business stakeholders.
Create dashboards, reports, and visualizations to support decision-making and operational improvements.

AI & Machine Learning

Design, develop, train, fine-tune, evaluate, and deploy machine learning models for enterprise business use cases.
Develop scalable AI/ML solutions using industry-standard frameworks and tools.
Monitor model performance, accuracy, and drift while implementing continuous improvement practices.
Collaborate with engineering and business teams to operationalize machine learning solutions.
Support MLOps initiatives, including model deployment, monitoring, and lifecycle management.

Generative AI & Intelligent Automation

Design and implement Generative AI solutions leveraging foundation models such as GPT, Claude, and other Large Language Models (LLMs).
Develop advanced prompt engineering frameworks to optimize AI response quality and business effectiveness.
Build Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources and vector databases.
Develop agentic AI workflows and intelligent automation capabilities to improve productivity and operational efficiency.
Integrate AI services and LLM capabilities into enterprise applications, APIs, and business processes.
Evaluate emerging AI technologies and identify opportunities for business adoption and innovation.

Software Development & Automation

Develop robust, scalable, and production-grade Python applications and automation solutions.
Maintain, enhance, and support existing automation frameworks and tools.
Build reusable services, APIs, and integration components supporting AI and data science initiatives.
Follow software engineering best practices including code reviews, testing, CI/CD, and documentation.
Collaborate with development teams to embed AI and analytics capabilities into enterprise platforms.

Performance Engineering & Optimization

Design and execute load, scalability, stress, and performance testing for applications, APIs, AI models, and data processing pipelines.
Establish performance benchmarks, KPIs, and operational metrics for AI and data systems.
Analyze performance bottlenecks and partner with engineering teams to implement optimization strategies.
Implement monitoring, observability, and alerting solutions for production AI and analytics workloads.
Ensure reliability, scalability, and operational efficiency of deployed AI solutions.

Collaboration & Innovation

Partner with business stakeholders, product teams, architects, data engineers, and developers to deliver AI-driven business outcomes.
Present analytical findings and AI recommendations to technical and non-technical audiences.
Document data science methodologies, AI architectures, models, and operational procedures.
Lead proof-of-concept (POC) initiatives and contribute to enterprise AI and analytics strategy.
Stay current on advancements in AI, machine learning, data science, cloud technologies, and industry best practices.


Required Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related field.
Strong experience in Data Science, Machine Learning, Artificial Intelligence, and Python development.
Expertise in statistical analysis, predictive modeling, and data mining techniques.
Experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, or similar technologies.
Hands-on experience with Large Language Models (LLMs), Generative AI, Prompt Engineering, RAG, and AI Agents.
Experience in data visualization and storytelling using analytical tools and dashboards.
Strong programming, problem-solving, and analytical skills.
Experience with API development, automation frameworks, and software engineering best practices.
Knowledge of cloud platforms such as Azure, AWS, or GCP.