Principal Software Engineer - AI

Responsibilities: 

• Hands-on Principal AI/ML engineer, driving technical innovation  

• Partner with product owners to define visionary AI features  

• Collaborate cross-functionally to assess impacts of new AI capabilities  

• Consult and guide teams to productize prototypes with provable accuracy  

• Lead research and selection of COTS and development of in-house AI/ML technologies  

• Evaluate foundational models and emerging AI advancements  

• Explore new technologies and design patterns through impactful prototypes  

• Present research and insights to inspire innovation across teams  

• Guide, design and testing of agentic workflows and prompt engineering 

• Fine-tune models, validate efficacy with metrics, and ensure reliability  

• Evaluate and guide synthetic data generation for training and validation  

• Design and guide scalable data pipelines for AI/ML training and inference  

• Oversee data analysis, curation, and preprocessing  

• Collaborate with external partners on AI development and integration  

• Establish AI design best practices and standards for alignment 

• Contribute to patentable AI innovations 

• Utilize and apply generative AI to increase productivity for yourself and the organization 

Environment, Tools & Technologies: 

• Agile/Scrum 

• Operating Systems – Mac, Linux 

• JavaScript, Node.js, Python 

• PyTorch, Tensorflow, Keras, OpenAI, Anthropic, and friends 

• Langchain, Langgraph, etc. 

• APIs GraphQL, REST 

• Docker, Kubernetes 

• Amazon Web Services (AWS), MS Azure 

• Sagemaker, NIMS 

• SQL: Postgres RDS 

• NoSQL: Cassandra, Elasticsearch (VectorDb) 

• Messaging – Kafka, RabbitMQ, SQS 

• Monitoring – Prometheus, ELK 

• GitHub, IDE (your choice) 

Skills & Qualifications:  

• 8+ Years of Relevant Industry Experience (with a Master’s Degree Preferred) 

• Experience with LLMs and fine-tuning foundation models 

• Development experience including unit testing 

• Design and documentation experience of new APIs, data models, service interactions 

• Familiarity with and ability to explain: 

• Agentic AI development and testing 

• AI security and data privacy concerns 

• Synthetic data generation and concerns 

• Foundation model fine-tuning 

• Generative AI prompt engineering and challenges