ML Software Engineer

You will operate as a hands-on engineering leader responsible for designing building and running production-grade ML and Generative AI services while setting technical direction that scales across multiple workstreams. You will remain close to the code and architecture decisions establish delivery and engineering standards and ensure solutions meet enterprise expectations for security stability and operational rigor.

A core requirement is stakeholder partnership: you will routinely explain what is being built why it matters and how it will perform in production to both technical and non-technical audiences enabling informed decisions and clear delivery alignment.

Job responsibilities

• Provide hands-on technical leadership by designing developing and deploying ML/LLM/GenAI solutions from concept through production maintaining ownership for reliability and operability once deployed

• Work closely with product managers data scientists ML engineers and other stakeholders to understand requirements and prioritize use cases.

• Mentor and uplift junior engineers through design reviews code reviews pairing and coaching raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities including automated pipelines for deployment monitoring and model lifecycle management with emphasis on scalability and reliability

• Implement optimization strategies to fine-tune generative models for specific NLP use cases ensuring high-quality outputs in summarization and text generation.

• Conduct thorough evaluations of generative models (e.g. GPT-4.1) iterate on model architectures and implement improvements to enhance overall performance in NLP applications.

• Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.

• Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.

• Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research implement cutting-edge techniques and leverage external APIs for enhanced functionality.

Required qualifications capabilities and skills

• Bachelors or Masters degree in Computer Science Engineering or a related field

• 10 years of engineering experience including 3-5 years building deploying and operating applied AI/ML systems in production (model lifecycle MLOps monitoring and governance).

• Demonstrate hands-on engineering leadership: setting technical direction making architecture decisions conducting design and code reviews mentoring junior engineers and guiding implementation quality across multiple workstreams

• Proficiency in programming languages like Python for model development experimentation and integration with OpenAI API.

• Experience with machine learning frameworks libraries and APIs such as TensorFlow PyTorch Scikit-learn and OpenAI API.

• Experience with cloud computing platforms (e.g. AWS Azure Google Cloud Platform Snowflake or Databricks) containerization technologies (e.g. Docker and Kubernetes) and microservices design implementation and performance optimization.

• Solid understanding of fundamentals of statistics machine learning (e.g. classification regression time series deep learning reinforcement learning) and generative model architectures particularly GANs VAEs.

• Ability to design Agentic AI architecture to solve complex problems including context engineering and RAG.

• Ability to identify and address AI/ML/LLM/GenAI challenges implement optimizations and fine-tune models for optimal performance in NLP applications.

• Strong collaboration skills to work effectively with cross-functional teams communicate complex concepts and contribute to interdisciplinary projects.

• A portfolio showcasing successful applications of generative models in NLP projects including examples of utilizing OpenAI APIs for prompt engineering.

Preferred qualifications capabilities and skills

• Familiarity with the financial services industries.

• Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).

• Hands-on knowledge of Chain-of-Thoughts Tree-of-Thoughts Graph-of-Thoughts prompting strategies.