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Roles And Responsibilities
Does (The tasks / responsibilities that the role performs to address requirements in Key Result Areas)
Builds and tests AI prototypes (predictive models, generative AI, agentic workflows) to validate feasibility.
Experiments with emerging AI tools, APIs, and frameworks to identify enterprise fit.
Supports the AI Architect in codifying learnings into playbooks, reusable patterns, and reference implementations.
Conducts benchmarks, evaluations, and technical spikes on AI technologies.
Collaborates with AI CoE to transition successful prototypes into scalable delivery models.
Contributes to the creation of technical briefs and white papers, documenting architectural insights and lessons learned.
Displays (The Knowledge, Skills, and Behaviors indicating how tasks / responsibilities will be performed)
Strong hands-on skills in Python, ML frameworks (TensorFlow, PyTorch), GenAI APIs (OpenAI, Anthropic, etc.), and agent toolkits.
Maker/experimental mindset, with ability to quickly test, fail fast, and iterate.
Analytical rigor in benchmarking models, measuring accuracy, latency, cost efficiency.
Growth mindset: proactive learning of latest AI research and industry trends.
Effective communicator: translates technical findings into clear architectural insights.
Strong stakeholder management and the ability to influence executives and delivery teams.
Strong sense of ownership and accountability when leading projects.
Thoughtfulness and thoroughness when participating in the design and evaluation activities.
Systems thinking and Innovations mindset.
Solid understanding of software development methodologies (e.g., Agile, Scrum).
Strong analytical and problem-solving skills.
Ability to thrive in a fast-paced, collaborative environment.
Delivers (The specific outputs / tangible results produced by the role; resources responsible for)
Working prototypes and proof-of-concepts of AI capabilities.
Technical evaluation reports and benchmark results.
Contribution to reference implementations and reusable code libraries.
Documented lessons learned and recommendations for enterprise adoption.
Co-authored technical papers/white papers with AI Architect, reinforcing thought leadership.
QUALIFICATIONS (Minimum requirements in terms of experience and background, skills and competencies for sourcing purposes)
Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent.
3–6 years experience in AI/ML engineering, software engineering, or data science.
Proven hands-on expertise with ML frameworks, LLMs, RAG, and agent-based systems.
Experience with APIs, cloud platforms (AWS/GCP/Azure), and MLOps tooling.
Curiosity and ability to research and adapt quickly to emerging AI technologies.
Strong technical writing skills for documenting learnings and contributing to publications.
Strong written and verbal communication skills for executive and external representation.
What We Offer
Opportunity for career growth and development in the #1 FinTech company in the country Working with a dynamic and highly collaborative team who want to change the game A company that values their people with highly competitive and flexible compensation and benefits package
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