Responsibilities
- Contribute to the design and development of AI-powered platforms and systems, working alongside senior engineers on complex initiatives.
- Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration.
- Build AI-driven features, intelligent automation, and agentic systems that enhance customer experiences and improve team productivity.
- Embed AI capabilities into existing team workflows, products, and processes, ensuring solutions integrate naturally
- Integrate AI capabilities into customer-facing products, internal tools, and operational workflows.
- Build full-stack components spanning backend services, APIs, data layers, and frontend interfaces.
- Translate business and operational problems into maintainable, AI-enabled technical solutions with guidance from senior team members.
- Apply systems thinking to ensure reliability, performance, and security across platforms.
- Collaborate with engineers, teams, and stakeholders across the company to understand requirements and deliver impactful solutions.
- Participate in production support and incident response for team-owned systems, learning operational best practices.
- Engage in code reviews and knowledge sharing to continuously improve skills and contribute to team growth.
Requirements
- 1–3 years of professional experience in software engineering, AI engineering, platform engineering, or related roles.
- Hands-on experience designing, building, and shipping AI-powered systems into production environments.
- Strong proficiency in Python, SQL, and frontend frameworks such as React.
- Proven experience designing and owning production-grade full-stack systems.
- Experience working with distributed systems, internal platforms, or complex data workflows.
- Ability to operate effectively in ambiguous problem spaces and drive solutions with minimal direction.
Nice to Have
- Exposure to building AI-powered platforms or products, including contributing to architectural decisions.
- Foundational knowledge of core machine learning and data science principles, including supervised/unsupervised learning, feature engineering, and model evaluation.
- Familiarity with transformer architectures (BERT, GPT, etc.) and basic understanding of attention mechanisms, tokenization, and fine-tuning concepts.
- Exposure to embeddings and vector representations, including text embeddings and similarity search concepts; interest in vector databases (Pinecone, Weaviate, pgvector, or similar).
- Familiarity with NLP techniques such as text classification, named entity recognition, or semantic search.
- Familiarity with real-world agentic frameworks workflow engines (e.g., in-house automation platforms, langchain/graph, n8n, Make, or similar), with a solid understanding of how they work under the hood.
- Experience developing AI-assisted or agentic systems that interact with tools, data, or workflows.
- Familiarity with cloud-native development using AWS.
- Experience with containerized systems and orchestration tools such as Docker and Kubernetes.
- Exposure to CI/CD pipelines and modern development workflows (e.g., GitLab).
- Experience with Golang is a plus (or willingness to learn).
Additional Information
- Familiarity with Braze or similar consumer facing CRM platforms and/or various B2C digital marketing channels like affiliate, CTV, paid social, paid search, GEO/SEO, DSP.
