About the Role
We're seeking an exceptional AI/ML Engineer who breaks the traditional mold. This isn't a role for someone who only trains models or lives in Jupyter notebooks. We need an end-to end product engineer who happens to have deep AI/ML expertise someone who can architect scalable systems, ship production code, own product outcomes, and drive technical decisions from conception to deployment.
You'll be responsible for building and scaling AI-powered products that directly impact our users and business. This means taking models from research to production, designing robust APIs, optimizing infrastructure, collaborating with cross-functional teams, and owning the complete product lifecycle.
If you're a builder who thrives on seeing your work in users' hands and measures success by product impact rather than model accuracy alone, this role is for you.
What You'll Own
Product Development & Delivery
- You'll own entire AI/ML products from ideation to production. This includes defining technical architecture, making build-vs-buy decisions, scoping MVPs, and delivering features that solve real user problems.
- You'll work closely with product managers and designers, but you'll drive technical strategy and execution independently.
End-to-End ML Systems
- Design and implement complete ML pipelines including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
- You'll build systems that are maintainable, scalable, and production-ready not just experimental notebooks.
Production Engineering
- Write clean, tested, production-grade code across the stack.
- Build RESTful APIs, implement efficient data processing pipelines, optimize model serving infrastructure, and ensure systems are reliable, performant, and cost-effective at scale.
Technical Architecture
- Make critical architectural decisions around model selection, infrastructure design, data flow, and system integration.
- You'll evaluate trade-offs between different approaches, prototype solutions, and champion best practices across the team.
Cross-Functional Leadership
- Collaborate with engineering, product, design, and business teams to translate requirements into technical solutions.
- You'll advocate for users, communicate complex technical concepts clearly, and drive alignment on priorities and timelines.
Performance & Optimization
- Continuously improve system performance, model accuracy, latency, and resource utilization.
- Implement monitoring and observability to catch issues early, and iterate based on production metrics and user feedback.
What We're Looking For
Experience Profile
- 4-6 years of software engineering experience with at least 3 years building and deploying AI/ML systems in production environments.
- You've shipped real products that users depend on, not just research projects or POCs.
ML Engineering Excellence
- Strong fundamentals in machine learning with hands-on experience across multiple domains NLP, computer vision, recommendation systems, or time-series forecasting.
- You understand model selection, training strategies, evaluation metrics, and when to use different architectures.
- Proficiency with PyTorch or TensorFlow, scikit-learn, and modern ML frameworks.
Software Engineering Chops
- You're a strong programmer who writes clean, maintainable code.
- Solid proficiency in Python with experience in at least one additional language (Go, Java, JavaScript, or C++).
- Deep understanding of data structures, algorithms, design patterns, and software architecture principles.
Production ML Systems
- Proven track record building scalable ML infrastructure including model serving (TensorFlow Serving, TorchServe, ONNX), feature stores, experiment tracking (MLflow, Weights & Biases), and CI/CD for ML.
- Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure).
Full-Stack Capabilities
- Ability to build complete features end-to-end.
- Experience with backend development (FastAPI, Flask), API design, databases (SQL and NoSQL), caching strategies, and basic frontend skills when needed.
- You're comfortable working across the stack.
Data Engineering Skills
- Strong SQL and data manipulation skills with experience building ETL/ELT pipelines.
- Proficiency with data processing frameworks (Spark, Dask, or similar) and working with both structured and unstructured data at scale.
Product Mindset
- You think beyond technical implementation to user impact and business outcomes.
- Experience working closely with product teams, translating ambiguous requirements into technical solutions, and making pragmatic engineering decisions that balance quality, speed, and scope.
System Design
- Ability to design robust, scalable systems considering performance, reliability, security, and cost.
- Experience with distributed systems, microservices architecture, and handling high-traffic production environments.
Technical Stack Exposure
- Experience with modern LLM frameworks (LangChain, LlamaIndex, Haystack), vector databases (Pinecone, Weaviate, Qdrant), and RAG architectures is highly valued.
- Familiarity with model optimization techniques (quantization, pruning, distillation) and serving optimizations.
- Understanding of MLOps best practices and tools for model monitoring, versioning, and governance.
What Sets You Apart
- You've built AI features that thousands or millions of users interact with daily.
- You have strong opinions on engineering practices but remain pragmatic about trade-offs.
- You've mentored other engineers and elevated team standards.
- You're comfortable with ambiguity and can scope and execute projects with minimal guidance.
- You stay current with AI/ML advances but know when to use proven approaches versus cutting-edge research.
- You have experience with A/B testing and experimentation frameworks.
- You've dealt with model drift, data quality issues, and production incidents, emerging with better systems and processes.
What Success Looks Like
- In your first six months, you'll own at least one significant AI/ML feature from design to deployment, improve our ML infrastructure and development velocity, establish monitoring and evaluation frameworks for production models, and become a go-to technical resource for AI/ML product decisions across the organization.
- We're building products that require both deep technical expertise and strong product intuition.
- If you're excited about the intersection of AI/ML and product engineering, and you want to see your work directly impact users while working with cutting-edge technology, we'd love to hear from you.
