Software Engineer 2-GenAI/ML

About the Role

Customer Obsession engineering at Uber is looking for an experienced technologist who can architect, build, and scale our platform and core systems that power mission-critical user experiences including customer support, frictionless experiences & personalization. This would be a great engineer who has solved business problems at scale. L4 Engineer at Uber exhibits the following qualities:

1. Demonstrate tech expertise > Demonstrate technical skills to go very deep or broad in solving classes of problems or creating broadly leverageable solutions.
2. Execute large-scale projects > Subdivide the work into bi-weekly sprints and compartmentalize across functions or teams and ensure everyone knows how they fit in.
3. Collaborate across teams > Work across a group (multiple teams) to deliver results and are recognized for your high-quality communication and coordination abilities.
4. Coach engineers > Provide oversight and direction to various engineers working across different efforts.
5. Raise the bar > Improve the team's definition of best practices and architecture with deep domain knowledge

What the Candidate Will Need / Bonus Point

What the Candidate Will Do-

1. Actively seek out engineering problems and solve them with little to no guidance.
2. Own opportunities and initiatives and drive clarity, convergence, and closure across stakeholders.
3. Take the requirements and distill it down with the lead into tasks and milestones including trade-offs required to realize the vision.
4. Acting with good judgment and responsibility..
5. Provide technical expertise in designing, implementing, testing and rolling out the next generations of our fintech systems.
6. Raise the bar on sustainable engineering by improving best practices, producing best in class of code, documentation, testing, and monitoring.
7. Be a humble mentor and trusted advisor for both our talented team members and passionate leaders alike. Deal with differences in opinion in a mature and fair way, helping teams to commit and move forward.

Basic Qualifications

1. We’re looking for engineers with at least 3+ years of relevant experience building highly-scalable distributed systems. You have advanced knowledge of at least one programming language (Java, Go, C++) and at least basic knowledge of one or more of the following technologies: Kafka, NoSQL & relational databases, Redis, etc.
2. Fast learner. We’re looking for software engineers who thrive on learning new technologies and don’t believe in one-size-fits-all solutions. You should be able to adapt easily to meet the needs of our massive growth and rapidly evolving business environment.
3. Fearlessness. You think a working proof-of-concept is the best way to make a point. You strive on proving that speed and quality are not conflicting; that you can achieve both at the same time.
4. Versatility. In addition to having an intimate knowledge of the whole web stack, you understand how all the pieces fit together (front-end, database, network layer, etc.) and how they impact the performance of your application.
5. Architecture chops. You know how to build highly scalable, robust, and fault-tolerant services that support our unique rate-of-growth requirements.
6. Passion. You feel ownership over everything you ship; you'd never call code \"released\" until you’re confident it’s correct. You pride yourself on efficient monitoring, strong documentation, and proper test coverage

Preferred Qualifications

LangChain Framework Expertise:

1. Deep familiarity with LangChain library, its architecture, and components like chains, agents, tools, memory, and document retrieval.
2. Ability to design and implement custom chains and agents to solve complex business problems.
3. Experience with LangChain's vector stores (e.g., FAISS, Pinecone, Weaviate) for efficient document search and retrieval.

Large Language Models (LLMs):

1. Hands-on experience with LLM APIs (e.g., OpenAI GPT models, Cohere, Anthropic, or similar) and integrating them into LangChain workflows.
2. Proficiency in fine-tuning models or prompt engineering to improve the performance of language models within a LangChain framework.

NLP Techniques:

1. Understanding of NLP concepts such as tokenization, embeddings, semantic search, text generation, and text summarization.
2. Ability to leverage LangChain for tasks like question answering (QA), chatbots, semantic search, data extraction, and text-to-text transformation.