Senior Consultant - AI Innovation

Key Responsibilities:

AI Solution Development: Design, develop, and deploy advanced AI and machine learning models to solve diverse financial sector challenges, ensuring scalability and real-world impact.

Research & Innovation: Lead exploratory initiatives, experiment with novel methodologies, and adapt cutting-edge academic/industry research into practical, production-ready solutions.

Knowledge Contributions: Publish internal research outputs, contribute to whitepapers, and actively participate in knowledge-sharing sessions to drive thought leadership in AI for finance.

Engineering Excellence: Apply best coding practices, robust testing, and scalable software engineering principles to deliver reliable, deployment-ready AI assets.

Cross-Functional Collaboration: Partner with data engineers, product developers, and business teams to align solutions with client needs and project objectives.

Insight Communication: Simplify complex technical concepts into actionable insights, enabling non-technical stakeholders to make informed business decisions.

Continuous Learning and Development: Stay ahead of emerging AI technologies and proactively upskill through learning, experimentation, and applied research.

Skill Requirements:

Essential:

Analytical Problem-Solving and Innovative Thinking: Proven ability to analyze complex problems with creativity and rigor, developing effective and novel solutions.

Programming Proficiency: Competency in at least one modern programming language, preferably Python, with additional skills in R, Java, TypeScript, or C++ considered advantageous.

Production-Quality Development: Demonstrated interest in developing scalable, maintainable, and deployment-ready AI/ML models and software.

Database Expertise: Working knowledge of relational databases (e.g., MySQL) and/or non-relational (NoSQL) databases, including writing optimized queries and managing data effectively.

Solid Foundation in Machine Learning and Statistics: Deep understanding of statistical principles, core machine learning algorithms, and their practical implementation in cloud or on-premises environments.

Linux and DevOps Fundamentals: Basic familiarity with Linux OS, including usage of virtual environments (e.g., virtualenv), containerization technologies (e.g., Docker), and version control systems like Git.

Data Science Basics: Strong grasp of fundamental concepts in data structures, data preprocessing, and introductory areas such as Natural Language Processing (NLP), computer vision, and speech recognition.

Research-Oriented Mindset:

1.Enthusiastic about continuous learning, applied research, and staying updated with the latest technologies and methodologies.

2.Effective Communication and Team Collaboration: Ability to clearly convey technical concepts to diverse teams and work collaboratively in cross-functional environments.

3.Research Autonomy and Mindset:

4.Demonstrates scientific curiosity and creative problem-solving to independently design and deliver impactful research projects.

5.Exhibits critical thinking and analytical rigor to interpret complex data and ensure high-quality, reproducible results.

6.Adapts quickly to new tools, frameworks, and emerging AI technologies with a proactive learning approach.

7.Shows resilience in solving open-ended problems and thrives in ambiguity, maintaining focus and creativity even without clearly defined paths.

Preferred

Machine Learning & Data Science Libraries: Exposure to ML/DL libraries (e.g., TensorFlow, PyTorch) and basic concepts in Data Science, Machine Learning, and Deep Learning.

Generative & Agentic AI: Experience with open-source tools such as Hugging Face, Lang Chain, and MCP, with an understanding of prompt engineering and agentic AI frameworks for building intelligent, autonomous solutions is a plus.

Cloud Computing: Familiarity with cloud platforms like AWS, Azure, or GCP.

Research Publications: Authorship or co-authorship of peer-reviewed research papers, whitepapers, or patents relevant to data science, AI, or the financial domain is highly desirable and considered an advantage for this role.

Applied Research Evidence: Demonstrated track record in presenting research at conferences, or contributing to open-source research projects and toolkits, will be viewed positively

Projects & Certifications: Prior projects, internships, or certifications related to data processing, analytics, cloud computing, or relevant courses in data, cloud, or analytics.