Chennai, Tamil Nadu, India
Information Technology
Full-Time
Uber
Overview
Senior Software Engineer, AI Center of excellence
Uber's AI Center Of Excellence For Security And Privacy Is Dedicated To Building Robust, Scalable AI-driven Products, Engineering Standards, And Policies That Proactively Safeguard User Data, Fortify Our Security Posture, And Foster Trust Through Transparent Communication And Innovation. As a Leader On This Team, You Will
Uber's AI Center Of Excellence For Security And Privacy Is Dedicated To Building Robust, Scalable AI-driven Products, Engineering Standards, And Policies That Proactively Safeguard User Data, Fortify Our Security Posture, And Foster Trust Through Transparent Communication And Innovation. As a Leader On This Team, You Will
- Shape Strategy: Define and drive the roadmap for AI/ML-powered security and privacy solutions that span user-facing applications, downstream services, and core infrastructure platforms.
- Architect & Build: Lead the end-to-end design, development, and deployment of high-impact tools and frameworks that integrate seamlessly across Uber's ecosystem.
- Collaborate & Evangelize: Partner with product, engineering, legal, and policy teams to translate complex security and privacy requirements into practical AI solutions-and champion best practices across the organization.
- Mentor & Grow: Coach engineers and data scientists in secure ML development, threat modeling, and privacy-preserving techniques, helping to elevate the team's technical expertise.
- End-to-End AI/ML & GenAI Expertise: Proven track record designing, developing, and deploying production-quality AI/ML solutions-especially generative AI systems-spanning data ingestion, prompt engineering, model fine-tuning, retrieval-augmented generation (RAG), and inference optimization.
- Large Language Model Proficiency: Hands-on experience with foundation models (e.g., GPT, PaLM, LLaMA) and open-source alternatives; adept at prompt design, chain-of-thought engineering, embedding creation, and custom fine-tuning workflows.
- Agent Development & Orchestration: Experience building and deploying AI agents-designing multi-step workflows, tool integrations, and autonomous decision-making pipelines to solve complex tasks and drive business value.
- GenAI Infrastructure & MLOps: Familiarity with MLOps pipelines for GenAI (model versioning, CI/CD, monitoring, A/B testing), container orchestration (Docker/Kubernetes), and scalable deployment of LLMs in cloud or on-prem environments.
- Data & Vector Store Management: Ability to build and maintain scalable data pipelines and vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient semantic search and knowledge retrieval.
- Robust Software Engineering: Strong coding skills in Python (and/or Java, Go) with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn) and building secure, maintainable APIs and microservices.
- LLM Fine-Tuning Expertise: Hands-on experience fine-tuning large language models using techniques such as LoRA, PEFT, or full-parameter updates on both open-source and proprietary models.
- Agent Frameworks: Prior work building or extending agent frameworks (e.g., LangChain, LlamaIndex, or custom in-house solutions) for multi-step reasoning and tool orchestration.
- Prompt Engineering Mastery: Deep familiarity with prompt templating, few-shot/zero-shot strategies, chain-of-thought prompting, and mitigation of prompt drift or hallucinations.
- GenAI Evaluation & Testing: Experience designing evaluation protocols for generative models (automated metrics and human-in-the-loop testing), with an emphasis on security, bias detection, and reliability.
- Define & Drive AI Security/Privacy Solutions: Architect and implement end-to-end GenAI and ML systems-agents, RAG pipelines, fine-tuned LLMs, and custom models-that proactively detect and mitigate security threats and privacy risks.
- Build & Operate Platforms: Develop scalable data pipelines, vector stores, and MLOps infrastructure (CI/CD, monitoring, model-versioning) to support continuous delivery of AI-driven security and privacy features.
- Integrate with Uber Ecosystem: Embed AI capabilities into user-facing products, downstream services, and core infrastructure tools-designing APIs, microservices, and tooling for seamless adoption by engineering teams.
- Collaborate Cross-Functionally: Partner closely with Product, Legal, Policy, and Security Operations to translate requirements into technical specs, ensure compliance with data-privacy regulations, and align on risk-mitigation strategies.
- Develop & Maintain Agents: Build autonomous AI agents and orchestration workflows that interface with internal systems and external tools, automating complex security and privacy tasks.
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