Staff Software Engineer - AI
Software Engineering, Data Science
Hyderabad, Telangana, India
Opportunity Management (OM) Team
About the Team
- Our mission is to deliver a resilient, internet-scale microservice platform supporting high-velocity development, real-time analytics, and intelligent automation. The team architects and powers the critical backend services at the core of our opportunity management and forecasting ecosystem. We collaborate closely with cross-functional leaders to drive innovation, enable data-driven decisions, and leverage cutting-edge AI/ML to ensure global customer impact.
- As Outreach invests in AI-native product experiences, this team owns both the backend platform and the clean, well-modeled, high-quality data foundations those experiences depend on. This role is central to that shift.
The Role
- We are seeking a Staff Software Engineer who thrives on tackling complex technical and architectural challenges at scale. You will bring deep expertise in scalable, distributed systems design, lead high-performing teams through ambiguity, and accelerate product innovation in data-driven and AI-rich environments. You will steer system evolution, guarantee performance under high load, and mentor peers while shaping delivery and technical direction.
- Equally important, you will raise the team’s data- and AI-engineering maturity — designing the pipelines, data models, and ML/GenAI integrations that power our next generation of intelligent features.
Your Daily Adventures Will Include:
- Leading the architecture, design, and delivery of distributed cloud-native applications capable of high concurrency and demanding real-time data needs.
- Designing and building production-grade data pipelines and ETL/ELT workflows — modeling data for both transactional (OLTP) and analytical (OLAP) use, and orchestrating them with modern workflow tooling.
- Integrating ML and GenAI capabilities into product features — from model serving and evaluation to LLM-powered enrichment, retrieval (RAG), and intelligent automation within our services.
- Championing data quality and correctness — building validation, observability, and testing into every step of the pipeline so downstream analytics and AI features can be trusted.
- Collaborating with data science, product, and engineering partners to ship intelligent, complex product features.
- Setting and promoting engineering standards for code quality, security, and operational excellence; nurturing automation and continuous improvement.
- Diagnosing and eliminating performance bottlenecks and proactively addressing reliability risks.
- Mentoring, reviewing code/architectures, and fostering a culture of rapid learning.
- Decomposing legacy systems into SOA/microservices, resolving tech debt, and evolving the architecture for scale.
- Taking end-to-end ownership of major initiatives from planning through impact.
- Data modeling & storage: schema design, query optimization, and knowing when to reach for RDBMS, NoSQL, OLAP, or OLTP stores.
- Modern data stack: Spark / Delta Lake, Databricks or Snowflake, dbt for transformation, and Airflow (or similar) for orchestration.
- AI/ML in production: deploying and monitoring models, feature pipelines, and — increasingly — GenAI/LLM application patterns (embeddings, vector search, RAG, prompt/context engineering, evaluation).
- Analytics enablement: building the data models, frameworks, and artifacts that make trustworthy analytics and dashboards easy for others to build on.
What Sets This Role Apart: Data & AI Engineering
This role blends strong backend engineering with hands-on data and AI capability. You will be expected to grow the team’s fluency in these areas, so we look for depth (or a clear trajectory) across:
Our Vision of You:
- Demonstrated excellence designing and operating large-scale distributed systems with cloud service-oriented architecture.
- Proven leadership in fast-paced environments, setting standards, and inspiring technical teams to exceed delivery goals.
- Mastery in backend programming (Go required; Python strongly valued for data/AI work; Java/Ruby a plus) and hands-on with distributed data platforms (Kafka, RabbitMQ, NoSQL).
- Hands-on data engineering: building and operating data pipelines/ETL, data modeling and schema design, and a rigorous focus on data quality and correctness.
- Experience building APIs and analytics/data infrastructure, and deploying ML algorithms in production.
- Excellent communication and cross-team collaboration skills.
- Commitment to security, compliance, and robust, scalable design.
- Growth mindset — always learning, always elevating the technical bar for the team.
- Experience with the modern data stack: Spark/Delta Lake, Databricks or Snowflake, dbt, and Airflow.
- Hands-on GenAI/LLM application experience: RAG, vector databases, embeddings, prompt/context engineering, and model/output evaluation.
- MLOps exposure: feature stores, model serving, and monitoring in production.
- Experience shaping data as a product — dashboards, semantic/metrics layers, and analytics enablement for other teams.
Required Qualifications
Strong Pluses
Not required on day one, but a meaningful differentiator — and where we most want this hire to help the team grow.