Sr. Software Engineer - Applied AI Engineering
Bengaluru, IndiaFull-TimeMid-levelAI / Data Science
Responsibilities:
- On a day-to-day basis, you will be responsible to -
- Architect and lead the design of complex AI systems involving multi-agent orchestration, large-scale RAG pipelines, and production LLM infrastructure
- Own end-to-end delivery of critical AI features from conception to production, including design docs, implementation, and rollout strategy
- Drive technical direction for AI platform components: establish patterns, frameworks, and best practices across the engineering org
- Build high-scale data pipelines that process millions of records, manage embeddings at scale, and optimize for cost and latency
- Mentor SDE-II and junior engineers through code reviews, design discussions, and pairing sessions
- Lead evaluation and safety initiatives: design robust eval frameworks, implement guardrails, and ensure AI quality at scale
- Collaborate cross-functionally with Product, Data Science/Engineering, and Platform teams to shape roadmap and technical strategy
- Optimize production systems for performance, cost, and reliability; troubleshoot complex production issues
Skill Set:
- 5+ years of software engineering experience with 2+ years building production LLM/GenAI systems at scale
- Expert-level Python skills including async programming, performance optimization, and production-grade testing
- Deep hands-on experience with ML/LLM frameworks: PyTorch, Hugging Face, LangChain, LlamaIndex, or Ray
- Proven experience building and scaling vector search systems (Elasticsearch, Pinecone, Weaviate, FAISS)
- Strong system design skills: microservices, distributed systems, event-driven architectures (Kafka/SQS/Kinesis)
- Production experience with Kubernetes, Docker, and cloud infrastructure (AWS preferred)
- Expertise in LLM optimization: prompt engineering, fine-tuning, embeddings, RAG, token management, and inference optimization
- Track record of technical leadership: driving architecture decisions, mentoring engineers, and shipping complex projects
- Excellent communication: able to influence technical decisions and collaborate effectively across teams
- Experience with high-throughput inference using Ray, Triton, vLLM, or TensorRT
- Background in LLM evaluation (RAGAS, custom harnesses, human-in-the-loop workflows)
- Deep knowledge of multi-agent systems and agentic workflows
- Experience optimizing AI costs at scale (prompt caching, batch processing, model selection)
- Contributions to open-source AI projects or published research
- Prior experience in high-growth startups or building 0-to-1 AI products
