Luma Financial Technologies

AI/ML Engineer

Remote AI ML Engineering role with clear candidate location fit.

PostedJul 4, 2026

Eligible countries1 accepted country

Seniority signalSenior

Work settingRemote

Accepted candidate locations

India

AWS CI/CD Docker Kubernetes Python

Role overview

AI/ML Engineer

Requirements and responsibilities

Readable role content extracted into sections for faster review.

Details

Lead AI Innovation in Fintech:
Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology.Implement GenAI, agent-based systems, and sophisticated ML models to enhance our platform capabilities.
Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology.
Implement GenAI, agent-based systems, and sophisticated ML models to enhance our platform capabilities.
Own the Full AI Lifecycle:
Design and implement robust data models that support AI/ML initiatives.Architect data pipelines to ensure seamless data integration and processing as part of the solution.
Design and implement robust data models that support AI/ML initiatives.
Architect data pipelines to ensure seamless data integration and processing as part of the solution.
Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology.
Implement GenAI, agent-based systems, and sophisticated ML models to enhance our platform capabilities.
Design and implement robust data models that support AI/ML initiatives.
Architect data pipelines to ensure seamless data integration and processing as part of the solution.
Develop scalable machine learning pipelines and data processing workflows.
Develop scalable machine learning pipelines and data processing workflows.
Scale AI in the Cloud:
Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus.Ensure robust deployment practices and maintain the performance and scalability of AI systems.
Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus.
Ensure robust deployment practices and maintain the performance and scalability of AI systems.
Specialized Projects:
Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial [JP1] scenarios.Develop and enhance OCR capabilities and integrate these with vector databases.Utilize Retrieval Augmented Generation (RAG) techniques to improve data retrieval and decision-making processes.
Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial [JP1] scenarios.
Develop and enhance OCR capabilities and integrate these with vector databases.
Utilize Retrieval Augmented Generation (RAG) techniques to improve data retrieval and decision-making processes.
MLOps Integration (Plus):
Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models.Collaborate with DevOps teams to optimize and monitor production-level AI/ML solutions.
Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models.
Collaborate with DevOps teams to optimize and monitor production-level AI/ML solutions.
Be a Thought Leader:
Provide technical guidance and mentorship to team members.Engage in knowledge-sharing sessions to drive continuous improvement across the team.Stay abreast of the latest advancements in AI/ML research, tools, and best practices.Experiment with novel prompting techniques and refine model architectures for improved outcomes.
Provide technical guidance and mentorship to team members.
Engage in knowledge-sharing sessions to drive continuous improvement across the team.
Stay abreast of the latest advancements in AI/ML research, tools, and best practices.
Experiment with novel prompting techniques and refine model architectures for improved outcomes.
Develop scalable machine learning pipelines and data processing workflows.
Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus.
Ensure robust deployment practices and maintain the performance and scalability of AI systems.
Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial [JP1] scenarios.
Develop and enhance OCR capabilities and integrate these with vector databases.
Utilize Retrieval Augmented Generation (RAG) techniques to improve data retrieval and decision-making processes.
Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models.
Collaborate with DevOps teams to optimize and monitor production-level AI/ML solutions.
Provide technical guidance and mentorship to team members.
Engage in knowledge-sharing sessions to drive continuous improvement across the team.
Stay abreast of the latest advancements in AI/ML research, tools, and best practices.
Experiment with novel prompting techniques and refine model architectures for improved outcomes.
Experience:
Minimum 3 years of professional experience in AI/ML engineering or a related field.Must have hands-on experience working on a commercial product that is already in production.
Minimum 3 years of professional experience in AI/ML engineering or a related field.
Must have hands-on experience working on a commercial product that is already in production.
Technical Expertise:
In-depth knowledge of GenAI, agent-based systems, ML models, and prompting techniques.Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG).Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
In-depth knowledge of GenAI, agent-based systems, ML models, and prompting techniques.
Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG).
Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Cloud Experience:
Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required.
Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required.
Soft Skills:
Strong problem-solving abilities.Excellent communication skills and a collaborative mindset.Ability to thrive in a fast-paced, innovative environment.
Strong problem-solving abilities.
Excellent communication skills and a collaborative mindset.
Ability to thrive in a fast-paced, innovative environment.
Minimum 3 years of professional experience in AI/ML engineering or a related field.
Must have hands-on experience working on a commercial product that is already in production.
In-depth knowledge of GenAI, agent-based systems, ML models, and prompting techniques.
Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG).
Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required.
Strong problem-solving abilities.
Excellent communication skills and a collaborative mindset.
Ability to thrive in a fast-paced, innovative environment.
Education:
Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline.
Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline.
Financial Technology Exposure:
Experience in the financial technology sector, particularly with structured products or annuities.
Experience in the financial technology sector, particularly with structured products or annuities.
Additional Technical Skills:
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.Experience with DevOps best practices and contributing to open-source projects.
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
Experience with DevOps best practices and contributing to open-source projects.
Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline.
Experience in the financial technology sector, particularly with structured products or annuities.
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
Experience with DevOps best practices and contributing to open-source projects.